Sports and concert event ticket pricing and visualization system

ABSTRACT

A system and method for displaying seat inventory at a venue and facilitating planning of ticket prices for events at the venue is presented. Methods to predict total revenue for an event are described. Also presented are systems and methods for determining at what price and when to release so-called ‘flex’ price tickets during an on-sale using the sales velocity and sales/inquiry ratios. Determining demand of seats from secondary markets is also described with methods to use the demand for either re-pricing the seats in the primary market or presenting ‘best value’ seats to a prospective purchaser.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application claims priority to and is a continuation-in-partof U.S. patent application Ser. No. 15/260,208, filed Sep. 8, 2016,which is a continuation of U.S. Continuation-in-Part application Ser.No. 14/139,814, filed Dec. 23, 2013, which is a continuation-in-part ofU.S. Non-provisional patent application Ser. No. 13/789,581, filed Mar.7, 2013, which is a non-provisional of U.S. Provisional PatentApplication Ser. No. 61/617,238, filed Mar. 29, 2012, and U.S.Provisional Patent Application Ser. No. 61/613,962, filed Mar. 21, 2012.Also, U.S. application Ser. No. 13/789,581 is a continuation of U.S.application Ser. No. 13/585,714, filed on Aug. 14, 2012, which is acontinuation of U.S. application Ser. No. 13/355,453, filed on Jan. 20,2012, which is a division of U.S. application Ser. No. 12/422,171, filedon Apr. 10, 2009, which is a continuation of PCT/US09/35024, filed onFeb. 24, 2009, and claims priority to and is a non-provisionalapplication of U.S. Provisional Patent Application Ser. No. 61/031,020,filed Feb. 25, 2008, U.S. Provisional Patent Application Ser. No.61/055,142, filed May 22, 2008, U.S. Provisional Patent Application Ser.No. 61/098,765, filed Sep. 20, 2008, and U.S. Provisional PatentApplication Ser. No. 61/114,463, filed Nov. 14, 2008, the disclosures ofwhich are each hereby incorporated by reference in their entirety forall purposes.

BACKGROUND OF THE INVENTION

The present application generally relates to data processing infinancial, business practice, management, or cost/price determination inreservation, check-in, and booking display for reserved space. Systemsand methods for sales, pricing, and distribution of tickets for concert,sports, and other events are presented. More specifically, the presentinvention relates to a system and method for facilitating the pricing oftickets at a venue prior to the event, displaying seat inventory at thevenue, determining demand for tickets before, during, and after aninitial on-sale, and automatically determining if prices should bechanged or inventory should be redirected to a different distributionchannel based on the demand for the tickets. The present applicationalso relates to determining optimally valued tickets for purchase by aconsumer and determining the appropriate customer for that ticket.

Computer systems and networks have facilitated the task of buying,selling, and transferring goods. For example, global computer networks,such as the Internet, have allowed purchasers to quickly and efficientlyseek and purchase goods on-line. Buying and selling tickets online toevents at sports stadiums, arenas, theaters, entertainment clubs, andother venues has become a multi-billion dollar industry.

The accurate pricing of tickets is sometimes critical to achieve maximumrevenues for an event. Prices that are too high will curb demand, whileprices that are too low will create ample demand but at a non-optimalprice. For sports teams, prices are typically determined at thebeginning of a season, and pricing adjustments are made throughpromotions, give-aways, or other mechanisms. However, the face value ofthe tickets often remains unchanged. For concerts and other “one-off”events, pricing can be independent of other events.

The relative pricing of tickets within a venue is also a challenge.Currently, prices are typically determined by their ‘section’ in avenue. The closer, more centered, and less obstructed the view of theevent from the section, the better the seat and the higher the ticketprice. Seats with comparable views can be considered seats of comparableseat quality. In some venues, floor tickets in a given section will bepriced higher than the rest of the seats in that section. Today, thesepricing decisions are typically made based on personal experience ofpromoters, venue representatives, and other industry professionals.

In a venue, a ‘seat’ is not necessarily a chair, bench, or otherapparatus upon which one sits down. Instead, a seat can include an openspace for a wheelchair, stroller, or similar conveyance, a position in ageneral standing area, a place to bring one's own chair and picnicbasket, or other definitions as known in the art. A seat can alsoinclude a parking place for drive-in theater, a dock along a log-boomfor watching a hydroplane race, a rail upon which to tie up a horse, orother positions upon which a vehicle or conveyance can be parked,anchored, or moored. For clarity and simplicity in explanation,individual seats will be referred to in the examples of thisspecification, although the broader term is certainly envisioned.

There are many types of revenue management challenges in sports andentertainment planning, including how to price events. Pricing an eventcommonly occurs in advance of the beginning of ticket sales, but canalso occur over time during the sale period of tickets and even duringthe beginning of the event. Other challenges related to pricing includedeciding what discounts should be offered or what premiums should becharged, to whom and when to offer or charge the discounts or premiums,and issues around grouping events into packages. Ticket packages caninclude multiple tickets for different events at the same venue anddifferent events at different venues. Packages can also include ticketssold for the same event at the same venue for large parties, for examplegroup discounts.

Selling events in bundles as season or partial-season tickets is animportant revenue management area, especially for sporting events.Customers who purchase bundles are committing to multiple events, but atreduced per-event ticket prices. The customers are assured that theywill be able to attend events throughout the season. Season tickets mayyield revenue benefits for the selling organization, such as early cashflow and reduced risk. Thus, bundled ticket products are prioritized andare generally sold first in the selling season, while individual ticketsfor those events are made available at a later date. Determining theproper mix of bundled sales and single seat sales can be an importantdecision.

Often, pricing is determined based on a total or net revenue targetassociated with the event rather than on demand for that particularevent. The pricing of events often involves the use of a spreadsheetwhich contains the number of seats in each section. Prices for eachsection are estimated and added to the spreadsheet, and the totalrevenue is calculated by multiplying the section price by the totalseating capacity for that section. Pricing is altered until a certaintargeted total revenue is met. From the spreadsheet, a venue map is thencolored by hand to give a visual representation of the seatingarrangement and corresponding price levels. The tradeoff betweensections, pricing levels, and other factors is tracked in the minds ofthe venue representatives and promoters. The ability to visualize theseating arrangements in a venue map is not directly linked to theability to calculate financial information.

Ideally, prices would be established before an event takes place andthese prices would never need to be altered. However, it may also bedesirable to change prices once an event has gone on sale. If theinitial indication is that demand is greater than expected, it would bedesirable to raise prices. If demand is lower than expected, it may bedesirable to lower prices.

Recently there has been a growing interest in revenue managementsystems. This is particularly true for perishable products. A perishableproduct is one where the item has no value beyond a certain date. Oneobvious example is a food product susceptible to spoilage, but hotelrooms, airline seats, and event tickets are also examples of perishableproducts. See U.S. Pat. No. 7,020,617 issued Mar. 28, 2006 to Ouimertand U.S. Pat. No. 6,078,893 issued Jun. 20, 2000 to Ouimert et al., bothhereby incorporated by reference for all purposes. Airline seats andhotel rooms are particularly of interest in some recent studies. SeeU.S. Pat. No. 6,993,494 issued Jan. 31, 2006 to Boushy et al. and R.Preston McAfee and Vera to Velde, “Dynamic Pricing in the AirlineIndustry,” (Pasadena, Calif.: California Institute of Technology,undated), 44 pages. These systems use past history and current inventorydata to manage revenue and profit.

More recently, there has been an attempt to apply these types of systemsto sports events and concerts. See U.S. Pat. No. 7,110,960 issued Sep.19, 2006 to Phillips et al., which is hereby incorporated for allpurposes. See also Welki, Andrew M. and Thomas J. Zlatoper, “USProfessional Football: The Demand For Game-Day Attendance in 1991,”Managerial and Decision Economics, Vol. 15, Issue 5, Special Issue: TheEconomics of Sports Enterprises (September-October, 1994) (New York:John Wiley & Sons, 1994), pages 489-495. See also Drake, M. J., S.Duran, P. M. Griffin, and J. L. Swann, “Optimal timing of switchesbetween product sales for sports and entertainment tickets,” NavalResearch Logistics, Vol. 55, Issue 1, (New York: Wiley Periodicals,Inc., 2007), pp. 59-75. Determining pricing for sports events issometimes more challenging than pricing airline seats and hotel roomsbecause there is more consistency in the airline or hotel industry. Forexample, typically an airline will fly the same plane the same day ofthe week at the same time to the same destination. Past history is agood indicator of future demand. In sports, however, demand is dependenton many factors including the opponent, the day of the week, if a playergets injured, or even the weather. Most past effort has focused onestablishing the proper relationship between the many variables anddemand. These systems can be very complex; thus, demand is stilltypically estimated by sales and marketing personnel based on their ownpast experience and intuition. This challenge is further complicated asthe value of a ticket is also dependent on the location within the venue(as compared to an airline where all coach seats traditionally areconsidered of equal value).

Pricing for concerts and other “one-off” events can be more challengingthan sporting events where the same team may play multiple games in thesame venue. For these “one-off” events like concerts, boxing matches,ice shows, etc., pricing is often determined by targeting a specifictotal revenue assuming some portion of the seats sell. Promoters may usepast history to estimate demand, but often this data is old, andcustomer preference, economic factors, and other issues impacting demandmay have changed significantly for the current event relative to demandfor a prior event.

One key to all of these challenges is being able to determine demand forthe event, and then converting this demand into a fair price fortickets. Historically, tickets were only sold once, although there hasalmost always been “scalping,” or the ability to sell a ticket in theaftermarket. A recent proliferation of secondary marketing companies,particularly those that sell tickets on the web, has greatly increasedthe number of tickets that are resold. The availability of tickets inthe aftermarket has important implications for the sale of originaltickets. For example, tickets selling for a discount in the secondarymarket will negatively impact the sale of full price tickets in theprimary market. The original, or primary, ticket market encompasses allinstances in which event tickets are sold for the first time. Thesecondary ticket market encompasses all instances in which event ticketstrade after the original point of purchase.

Original and secondary event ticket markets are known in the art. SeeU.S. Patent Application Pub. No. 2006/0095344 published May 4, 2006 forNakfoor and U.S. Patent Application Pub. No. 2004/0093302 published May13, 2004 for Baker et al., both hereby incorporated for all purposes.

There are a large number of secondary ticketing sites that enable thepurchase of resold tickets through the Internet. Customers looking topurchase the ticket with the best overall value typically must browsefrom site to site and manually compare listings, both within one siteand across multiple sites.

Once pricing is established, effective marketing of those tickets to theright customer poses another challenge. Determining which potentialcustomers are most likely to purchase a specific type of product,whether in the primary or secondary market, can be difficult given thewide range of customers and varying and ever-changing interests of thepublic. Typically, customer analysis is done across all customers, butthe exact nature of customer interest in an event may depend not only onthe event but also on how much the customer is willing to pay for aticket to that event. The customer profile may also depend on where thecustomer wants to sit in the venue.

Thus, there is a need for a system that is capable of more accuratelyforecasting demand for events and optimizing pricing for that event.There is also a need for this system to facilitate the price planningand inventory tracking process for events. There is also a need for thissystem to provide recommended price changes once an event goes on sale.There is a further need for this system to be able to correlate thedemand to a specific customer demographic to aid in the marketing forthe event. Finally, it is desirable for this information to be viewed ina format that is easy to interpret.

There is also a need to clearly display available inventory to potentialpurchasers of primary and secondary seats where the value of prospectiveseats is also clearly displayed.

BRIEF SUMMARY OF THE INVENTION

Embodiments in accordance with the present disclosure relate toplanning, editing, tracking, recommending, and determining pricing anddistribution channels for event tickets. During the planning stages ofan event, an embodiment can (1) determine an estimate of pricing fromexternal data and (2) determine pricing for recurring events (e.g.,sports seasons). Pricing is determined by analyzing secondary marketdata, web traffic, and other variables. Another embodiment can (3) use aprice planning software tool to visualize and edit pricing. Price levelsare correlated to various seats for an event at a venue using a venuemap in a web-based environment in order to track potential revenuedynamically during the pricing process. Another embodiment (4) tracksinventory once the event goes on sale, either by (4a) current snap shotsor by (4b) sales over time. Inventory status is managed visually in thesame web-based environment by accessing an inventory database. Changesin inventory over time for an event can be visualized in graphical form,or a movie of how inventory changes with time can be created andanalyzed to determine current and future demand patterns. Demand can bedetermined using multiple methods, including gathering and analyzingprices for comparable seats in the secondary markets, determining salesvelocity in the primary market, and analyzing the correlation ofinquiries and seats sold. Yet another embodiment can (5) makerecommendations for pricing and distribution based on data around theon-sale. Prices can be adjusted accordingly to an analysis of the datato maximize revenues. The price of certain seats, rows, or sections canbe (5a) increased or decreased (‘flexed’) based on demand, or (5b)tickets can be redirected to the secondary market to increase revenues.These pricing mechanisms provide a means to dynamically match priceswith demand. Further, some embodiments include (6) an improved systemfor presenting secondary inventory for purchase. A further embodimentcan (7) match available inventory to the proper customer.

The numbering in the above paragraph and section headings below areadded for clarity and are not intended in any way to delineate featuresor aspects of the invention which must be represented in an embodiment.Many features are disclosed in this specification which may or may notfall within the scope of the headings. One should refer to the claims asissued in a patent by the Patent Office to determine the metes andbounds of the invention and use the entire disclosure to determine thelegal equivalents therein.

One embodiment in accordance with the present disclosure relates to acomputerized method for determining prices for an event at a venuehaving seats, the method comprising modeling two or more externalvariables including but not limited to web site traffic, radio playtime, prior sales, size of the city or region where the event will takeplace, venue size, the demographics of the city where the event will beheld, and the demographics of the customers that frequent the planned orsimilar events. The computerized method fits sales for a prior knownevent with one or more external variables to a mathematical model anddetermines pricing for a future event based on the values of the sameexternal variables for said future event.

Another embodiment relates to a computerized method for determiningticket packages for events, the method comprising determining, usingprincipal components analysis, an event quality for each of multipleevents, calculating an average event quality of the multiple events, andgrouping the events into two or more groups. The events are grouped intogroups of like event qualities. The method further includes packagingone or more events from one of the groups with one or more events fromanother of the groups such that an average of the packaged events issubstantially equal to the average event quality of the multiple events.“Substantially equal averages” are those which are generally equal,including those which are within ±10%, ±25%, or greater of each other.

Another embodiment relates to a computerized method for determiningpricing for multiple similar events, the method comprising receiving alist of seat tickets and corresponding prices for sale on a secondarymarket, filtering the list to remove outliers, determining the pricingor premium of the secondary market inventory for multiple similarevents, fitting the price or premium for each event to the total revenuegenerated for that event to a mathematical model, and determiningrevenue for a future event not used in the model based on the secondarypricing or premium for that event.

Another embodiment in accordance with the present disclosure relates toa computerized method for price planning an event at a venue havingseats, the method comprising providing price levels for an event at avenue, receiving rules to attribute the price levels to the seats, andcorrelating each price level with the seats according to the rules. Themethod further comprises displaying the price levels correlated with theseats on a venue map and calculating a total projected revenue for theevent using the price levels and a number of seats correlated with eachprice level.

Another embodiment relates to a computerized method for tracking anddisplaying a seat inventory of an event on a venue map, the methodcomprising providing a map of a venue, the map having graphics depictingseats, receiving a sales status of the seats from a database, anddisplaying the sales status of each seat with the corresponding seatgraphic on the map.

Another embodiment relates to a computerized method for tracking anddisplaying a seat inventory of an event on a venue map, the methodcomprising receiving a first status of an inventory of seats at a firstpoint in time, displaying the first status of the inventory of seats ona venue map, and receiving a second status of the inventory of seats ata second point in time. The method also includes updating the venue mapwith the second status of the inventory of seats on the venue map. Asequence of such updates may result in a movie.

Another embodiment relates to a computerized method for tracking anddisplaying a seat inventory of an event on a venue map, the methodcomprising receiving a first status of an inventory of seats at a firstpoint in time, receiving a second status of the inventory of seats at asecond point in time and providing a chart or graph of the inventory asa function of time.

Another embodiment relates to a computerized method for selectinginventory pricing for an event at a venue, the method comprisingdetermining a rate at which a first inventory of seats have sold for anevent at a venue and calculating a demand for a second inventory ofseats based on an algorithm which uses the rate at which the firstinventory of seats sold. The seats of the first and second inventoriesare comparable in quality. The method also includes determining one of aplurality of price levels at which to release the second inventory ofseats using the demand.

Another embodiment relates to a computerized method for selectinginventory pricing for an event at a venue, the method comprisingreceiving a first sales status for a first plurality of seats for anevent at a first time point, the first plurality of seats having a firstprice level, receiving a second sales status for the first plurality ofseats at a second time point, and calculating the number of seats thatwill be sold at some time in the future by analyzing the first salesstatus and the second sales status. The method further includesalgorithmically predicting a number of seats that could be moved from asecond price level to the first price level based on the predicteddemand of tickets at the first price level.

Another embodiment relates to a computerized method for determining thevalue of an available inventory of seats to an event if sold in thesecondary ticket market, the method comprising receiving a list of seattickets and corresponding prices listed for sale on a secondary market,grouping the seat tickets in the list by equivalent sections of seats,filtering the list to remove outliers, and fitting the prices of one ofthe groups of seat tickets as a function of row to a mathematical model.The method further includes calculating a potential price for seats ineach row from the mathematical model and determining whether inventoryof unsold seat tickets is priced lower than the calculated potentialprice. Still further, the method includes filtering the inventory ofprimary seat tickets based on the determination of whether the seat ispriced lower than the calculated potential secondary price anddisplaying the inventory that could achieve higher prices in thesecondary market than in the primary market.

Another embodiment relates to a computerized method for displayingavailable inventory of seats to an event for sale in the secondaryticket market, the method comprising receiving a list of seat ticketsfor sale on a secondary market, grouping the seat tickets in the list byequivalent sections of seats if desired, and determining one or morebest valued seats based on the seat price relative to the mathematicalmodel.

Another embodiment relates to a computerized method of determininglikely customers for a particular event ticket, the method comprisingassociating demographic descriptors to customers of a similar priorevent, correlating the demographic data to ticket price paid for theprior event, thereby determining the demographic profile of customers ofa future event as a function of ticket price.

In a specific embodiment, the present invention provides a method forestablishing preferences for an event from a mobile device. The mobiledevice comprises a display, a processing unit, and memory. The mobiledevice is configured to operate a plurality of applications, or apps, orother software programs. The method includes outputting an indication(e.g., button, link, graphic) on the display for a social networkingaccount from an event application. The method includes selecting a linkassociated with the social networking account to connect the eventapplication to the social networking account. The method includesoutputting a preference indication from the events application forinputting a plurality of preferences for a plurality of events. Themethod includes displaying a graphical indication associated with eachevent from the plurality of events. Each graphical indication includes asliding scale from a least desirable spatial region to a most desirablespatial region. The method includes selecting a degree of desirabilityranging from the least desirable spatial region to the most desirablespatial region on the indication of the event and repeating thedisplaying and the selecting for other events. The method includesassociating the degree of desirability for each of the events to thesocial networking account.

In a specific embodiment, the present invention provides outputting anevents list selector on the display from the event application. In otherembodiments, the degree of desirability comprises a rating from one tofive. In an example, the selecting the degree of desirability isprovided by a sliding scale button configured to the display. In anexample, the associating the degree of desirability comprising postingthe degree of desirability for each of the events to a page of thesocial networking account. Of course, there can be variations.

In an alternative specific embodiment, the present invention provides amethod to purchase tickets on a mobile device, which is coupled to acellular network, WiFi, WiMax, Blue Tooth, or other network. The networkis coupled to a world wide network of computers. The method includesinitializing a social networking application provided on a display ofthe mobile device, the social networking application and receiving logininformation from a user at a ticketing application from the socialnetworking application. The method includes retrieving a list ofinterests at the ticketing application from the social networkingapplication and retrieving a list of events at the ticketingapplication. The method includes processing the list of events to filterthe list of events to select a filtered listing of the events to beoutputted to the user of the mobile device and outputting the filteredlisting of events on the display of the mobile device. The methodincludes selecting an event from the list of events, retrieving aplurality of tickets for the selected event, and processing, undercontrol of a processor, the plurality of tickets for the selected eventsto select a listing of tickets. The method includes presenting on thedisplay the listing of tickets to the user on a venue image associatedwith the event and allowing the user to select at least one of thetickets in the listing. The method includes directing the user to aprocess for purchasing the selected tickets.

In an example, the method further comprising identifying a second userhaving a second list of interests, processing the list of interests ofthe user with the second list of interests of the second user, anddetermining whether the second list of interests and the list ofinterests are within a predetermined criteria. The method includesinviting the second user by transferring invitation informationinitiated from the ticketing application to a mobile device of thesecond user; whereupon the second user and the user are associated asfriends in the social networking application. In an example, the methodincludes transferring invitation information by the first user to one ormore users defined as friends in the social networking application.

In a specific embodiment, the present invention provides a method toidentify a desirable event and to a purchase ticket to the event on amobile device. The mobile device is coupled to a cellular network, WiFinetwork, satellite network, WiMax, Bluetooth, and other networks. Thenetwork is coupled to a world wide network of computers. The methodincludes retrieving a first list of first preferences from a first userfrom a first social networking site associated with the user, andretrieving a second list of second preferences from at least one moreperson associated with the first user of the first social networkingsite. The method includes processing, using a controller, the first listof first preferences with the second list of second preferences to forma collective list of preferences, which are characterized by the firstlist of first preferences and the second list of second preferences. Themethod includes receiving a list of events, and processing, using afilter process, the list of events with the collective list ofpreferences to output a filtered list of events. The method includesoutputting the filtered list of events on a second display of a secondmobile device of the second user and selecting an event from thefiltered list of events. The method includes retrieving a plurality oftickets for the selected event and processing the plurality of tickets,using a best value process, to filter the plurality of tickets toprovide a filtered listing of tickets. The method includes receivinginformation from a selected ticket from the filtered listing of ticketsand initiating a payment process for the selected ticket.

In an example, the first preferences or the second preferences areprovided from a database. The processing to form the collective list ofpreferences comprises location, likes, age, sporting events, and others.The filter process can include using a geolocation of the user. In anexample, the receiving of the listing of events is provided by text,email, or pop-up. In an example, the selecting of the event is providedby a button, voice, or other input device. The best value processcomprises a lowest price or best relative to other like locations in thevenue. In an example, the first user and the second user arecharacterized as friends.

In an alternative embodiment, the present invention provides a method ofdetermining selected users of a social networking site to invite to anevent from a plurality of events and of purchasing tickets to the eventusing a mobile device. The method includes retrieving a list of eventsfrom a ticketing application, selecting an event from the list ofevents, retrieving a first list of first preferences a first user, andassociating the first user with at least two users. The method includesdetermining a level of interest for each of the two users to attend theselected event and outputting a list of users interested in attendingthe selected event. The method includes retrieving a plurality oftickets for the selected event and processing the plurality of tickets,using a best value process, to filter the plurality of tickets toprovide a filtered listing of tickets. The method includes receivinginformation from a selected ticket from the filtered listing of ticketsand initiating a payment process for the selected ticket. In an example,the first user and the two users are characterized as friends.

To achieve these and other advantages, as embodied broadly and describedherein, a system and method to determine the optimal ticket in theoriginal and/or secondary market includes means for aggregatinginformation from the electronic ticket market and may include one ormore sites on the secondary market and/or one or more sites whereoriginal tickets are sold (e.g. a team's website, Ticket Master, etc).This information may include seat location (e.g. section, row, and seatnumber), number of seats available, price per ticket, ticket identifier,and other pertinent information.

In another aspect, the invention includes a system and method fordetermining the optimal ticket for purchase. The method includes a meansof adding the face value of the ticket to the information from thesecondary market where the face value of the ticket could include fullprice, season ticket price, current retail price, or other originalpricing information.

In another aspect, the invention includes a system and method fordetermining the optimal ticket for purchase. The method includes a meansof determining the premium or discount of the tickets in the secondaryticket market and/or original ticket market relative to the face value.

In another aspect, the invention includes a system and method fordetermining the optimal ticket for purchase. The method includes a meansof correlating the premium or discount for tickets in a specific sectionand/or original ticket price category with the row number therebyidentifying tickets that have a price that is lower than other ticketsin the same section relative to the row.

In another aspect, the invention includes a system and method fordetermining the optimal ticket for purchase. The method includesinputting purchaser preference information such as importance ofproximity to the event (i.e. low row number), importance of an aisleseat, importance of a particular side of the venue, importance of aspecific section, desire for a parking pass, or similar preferenceinformation.

In another aspect, the invention includes a system and method fordetermining the optimal ticket for purchase. The method includescombining the preference information with the pricing information toprovide the best available seat(s) for the purchaser.

The relationship between the group of events and the user's preferencesis determined and a ranking of event desirability is produced based onthe closeness of the event descriptors and user preference. Thiscomparison can be done using principal component analysis, discriminateanalysis, K nearest neighbors, or some other similar multivariateanalysis technique. It should be noted that one key feature of theapproach described above is that a user may be provided one or moreevents that are for a performer that is not known to the user but whichhas (have) a similar descriptor to the interests of the user.

The software can provide a rank list of events of interest but thesoftware may also have specific inventory information and can presentthe user with the ability to buy tickets to one or more of the eventsthat are determined to be a good fit. The software may provide amechanism to complete the transaction directly or may refer the user toa different site or location where the purchase can be completed. Thesoftware can further track the decision that a user makes based on theinitial set of recommendations and further refine the recommendationengine based on a user's ultimate decisions. [0049] In addition to beingable to find an event based on one user's preferences, this inventionalso provides means to provide recommended events based on thecombination of two or more peoples preferences. In addition, theinvention can rank other people's likely desire to attend a selectedevent based on their preferences. [0050] Yet other embodiments relate tosystems and machine-readable tangible storage media which employ orstore instructions for the methods described above.

A further understanding of the nature and the advantages of theembodiments disclosed and suggested herein may be realized by referenceto the remaining portions of the specification and the attacheddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a web page of a secondary market web site.

FIG. 2 illustrates a second web page of the secondary market web site ofFIG. 1.

FIG. 3 illustrates a venue map showing seats available.

FIG. 4 illustrates secondary market discounts/premiums of differentevents plotted versus total revenue.

FIG. 5 is a plot of secondary market discounts/premiums for variousevents versus total revenues for the events.

FIG. 6 is a plot of secondary market discounts/premiums for variousevents versus projected revenues for the events.

FIG. 7 illustrates a web page for defining price levels in accordancewith an embodiment.

FIG. 8A illustrates a web page for correlating price levels withsections of seats in accordance with an embodiment.

FIG. 8B illustrates a web page for correlating price levels withmultiple sections of seats in accordance with an alternate embodiment.

FIG. 9 illustrates a venue map showing price levels correlated withseats in FIG. 8A in accordance with an embodiment.

FIG. 10A illustrates a web page for editing price codes in accordancewith an embodiment.

FIG. 10B illustrates a web page for creating price codes in accordancewith the embodiment of FIG. 10A.

FIG. 10C illustrates a web page for viewing price codes and a relatedvenue map in accordance with the embodiment of FIG. 10A.

FIG. 11 is a flowchart with operations in accordance with an embodiment.

FIG. 12A illustrates a venue map showing statuses of inventories ofseats in accordance with an embodiment.

FIG. 12B illustrates an enlarged view of a portion of FIG. 12A.

FIG. 12C illustrates the venue map of FIG. 12A updated with statuses ofinventories of seats in accordance with an embodiment.

FIG. 12D illustrates the venue map of FIG. 12A showing allocations ofinventories of seats in accordance with an embodiment.

FIG. 13 is a flowchart with operations in accordance with anotherembodiment.

FIG. 14 is a plot of tickets sold and inquiries with respect to time inaccordance with one embodiment of the present invention.

FIG. 15 is a plot of total ticket sales for an event versus time.

FIG. 16 is a logarithmic plot of ticket sales per minute for an eventversus time.

FIG. 17 is a flowchart with operations in accordance with an embodiment.

FIG. 18 is a plot of tickets sold on the 100 level of a stadium withrespect to time in accordance with one embodiment of the presentinvention.

FIG. 19 is a plot of tickets sold versus number of inquiries atdifferent times for a particular event that can be used in accordancewith one embodiment of the present invention.

FIG. 20 illustrates a web page with final recommendations according toan embodiment.

FIG. 21A illustrates a table showing data and recommendations using analgorithm according to an embodiment.

FIG. 21B illustrates a table showing data and recommendations using thesame algorithm as in FIG. 21A.

FIG. 22A is a plot of secondary market discounts/premiums of specifictickets versus row number.

FIG. 22B is a table of the data plotted in FIG. 22A.

FIG. 23 is a plot of historical secondary market discounts/premiums foran event versus row number.

FIG. 24A is a plot of historical secondary market discounts/premiums ofside sections for an event versus row number.

FIG. 24B is a plot of historical secondary market discounts/premiums ofa center section for the same event as in FIG. 24A versus row number.

FIG. 25 is a flowchart with operations in accordance with an embodiment.

FIG. 26 illustrates a venue map showing seats available on a consumerwebsite in accordance with an embodiment.

FIG. 27 illustrates components of a computer network that can be used inaccordance with one embodiment of the present invention.

FIG. 28 illustrates components of a computerized device that can be usedin accordance with one embodiment of the present invention.

FIG. 29. Overview of the user flow of the improved ticket purchasingprocess.

FIG. 30. Overview of the backend flow for the improved ticket purchasingprocess.

FIG. 31 is an example of a list of tickets and a view from one of theseats in the list according to an embodiment.

FIG. 32 illustrates possible preferences listed in a questionnaire in anembodiment.

FIGS. 33 and 34 illustrate simplified flows of a process according to anembodiment of the present invention.

FIG. 35 shows a CMYK color spectrum and an RGB color spectrum accordingto an embodiment of the present invention.

FIG. 36 illustrates a method of using an outputted ticket to access agate to an event venue in accordance with one embodiment of the presentinvention.

FIG. 37 illustrates a method of using an outputted ticket to access adispenser within an event venue in accordance with one embodiment of thepresent invention.

The figures will now be used to illustrate different embodiments inaccordance with the invention. The figures are specific examples ofembodiments and should not be interpreted as limiting embodiments, butrather exemplary forms and procedures.

DETAILED DESCRIPTION OF THE INVENTION

The present application generally relates to data processing infinancial, business practice, management, or cost/price determination inreservation, check-in, and booking display for reserved space. Systemsand methods for sales, pricing, and distribution of tickets for concert,sports, and other events are presented. More specifically, the presentinvention relates to a system and method for facilitating the pricing oftickets at a venue prior to the event, displaying seat inventory at thevenue, determining demand for tickets before, during, and after aninitial on-sale, and automatically determining if prices should bechanged or inventory should be redirected to a different distributionchannel based on the demand for the tickets. The present applicationalso relates to determining optimally valued tickets for purchase by aconsumer and determining the appropriate customer for that ticket.

Even with the information provided on any given ticketing site, it isstill very difficult to compare available tickets to determine the bestvalue. Seats within any section often vary significantly in price andmany venues have multiple sections that offer similar views that must bemanually compared in order to determine the best price. Furthermore,pricing typically varies as the row number increases (i.e. the seat isfurther away from the field, stage, etc) and specific preferences suchas proximity to an aisle can change pricing and/or desirability. Findingthe optimal ticket for sale is even more complicated if one considersthe large number of sites that list tickets for original sale andre-sale. Locating the optimal ticket can be even further complicated bydifferent fees, shipping charges, and other costs that sites chargeabove and beyond the listed price.

Furthermore, the optimal ticket may depend on the desires of thepurchaser. For instance, one purchaser may be willing to spend a littlemore to be closer to the event while some other purchaser may want tospend less and be a little further away from the event.

Given the challenges in determining the relative value of tickets in theoriginal and/or secondary market, it is therefore desirable to provide asystem and method for identifying the optimal ticket for purchase.

Ticketing sites offer events at many different locations but comparingopportunities requires choosing a type of ticket (e.g. sporting event)and then finding one or more events that may be interesting and thencomparing ticket availability for events that may be of interest. Inaddition, events that occur at a time that is convenient may involveteams or acts that are not known to the buyer and it takes furthereffort to determine if these teams or acts are something that wouldinterest the would-be ticket buyer. Furthermore, some people areinterested in attending events that are being attended by friends oracquaintances and others are looking for an event where the would-beticket buyer is unlikely to encounter people that they know.

(1) Determining Prices

Planning for ticket prices for event or series of events, such asbasketball games, occurs weeks, months, or even years before the firstticket to the event is sold. Much of the planning is performed bypromoters and representatives of the venue where the event will be held.Other experienced professionals and stakeholders in the event industrymay also help in price planning.

In planning for an event, promoters and venue representatives determinewhat seats will be made available in the venue as inventory. In hashingout what ticket price levels (i.e., dollar amounts) to employ and whichseats should be sold at what price levels, the promoter and venuerepresentatives weigh a large amount of data relating to historicalprices, seating layouts, restricted areas, and setup eccentricitiescorresponding to the event at issue. Much of this is performed in theminds of the representatives based on experience with similar events or“last season's” prices. There are a multitude of variables to beconsidered in light of mass consumer preferences and expectations, notthe least of which is the layout of seating for the particular eventbeing planned.

Venues can typically be rearranged between different types of events,such as between sporting events and concert events. For example, asporting event may call for all seats to be available in the stands andno seats available on the field, while a concert event may fill thefloor of the arena with seats and kill or otherwise restrict from saleseats behind a stage. Some sporting events have different seatinglayouts than others. For example, basketball, which features seats nearthe ground and close to the action, typically has a different seatingarrangement than football, which features seats farther away from thefield. Some events, such as soccer and football, may have equal seatingarrangements. Likewise, different concerts and entertainment events mayhave similar or different layouts, depending on the artists involved,types of performance, or types of production. Seating layouts may bepredetermined by the promoter, venue, or artist, or the seating layoutmay be determined in conjunction with pricing.

Often, the first step in planning an event is to determine pricing.Sometimes there is not adequate data associated with the event toprovide accurate pricing. For example, a particular band may not havetoured for several years. In this case, data for a similar act can beused to augment the information. The similar act can be determined bycomparing key variables of the act of interest with those of otherbands. Key variables may include the type of music the band performs aswell as the audience demographics the band attracts. These variables canassociated with a cardinal or ordinal value and be compared usingclustering software, such as principal component analysis (PCA). In thisway, past events similar to those of the current event can bequantitatively recognized and presented.

After past events are recognized, revenue and other demand-indicatingdata from those events can be analyzed to estimate current demand forthe event being planned.

A mathematical or computerized simulation model can be used to estimatethe demand. Key variables that can be incorporated into the model mayinclude venue size, demographics of the act's fans, demographics of thecity for the event, the population of the city or area where the eventis to be played, the number of Internet searches for the artist from thecity or area where the act has played and plans to play, the frequencywith which the artist's music is played on the radio, and the number ofother events that occurred or are planned to occur at the same time andin the same city of the event of interest.

In one analysis performed by the inventors, the most important variablesto determine demand were determined to be total population of the cityfor the event, the number of on-line searches for the artist in the cityof the event, and the radio play time for the artist in the city of theevent. By fitting these variables to total concert revenue in citieswhere the act or comparable act had already performed, a model wasgenerated and then applied to cities where the act had not yetperformed. By using the population, search information, and radio playtime for these new cities, a projected revenue was calculated. Thistotal revenue was then used to determine average ticket pricing based ondifferent seating configurations (i.e., projected revenue/number ofseats=price per seat).

Thus, determining demand for an event such as a concert can beanalytically determined. Such a determination may be proceduralized andthen optimized to create the best pricing levels for an event.

(2) Pricing Multiple Events—Season Tickets and Packages

Pricing multiple events, such as a season of football games, presents adifferent set of challenges than pricing for individual events. Forsports events that feature a whole season of home games played at thesame venue, season tickets and group packages complicate demand andpricing models because of cross-coupling between games at differenttimes of the year, different nights of the week, and playoff and regularseason games.

At times it may be helpful to create groupings of events based on thedesirability of the events or so-called ‘event quality.’ This can beused in the creation of bundling plans where highly desirable events arecombined with less desirable events to create mini-season ticket plans.This is particularly true for sports teams that play multiple games eachseason. In order to properly group events, it is often important toestablish the relative value for each event or game. One method todefine event quality is by using a clustering analysis such ashierarchical cluster analysis or principal components analysis (PCA).This technique allows demand oriented variables to be analyzedsimultaneously to provide an indication of similarity across theseevents. Key variables could include historical data such as totalrevenue, group revenue or sales on the day prior to the event from thepreceding year. The PCA can also use additional non-revenue basedinformation that may impact revenues, such as changes in personnel sincethe revenue data was collected, etc. These factors can sometimes createa significant change in event desirability or game quality. Principalcomponent analysis is then used to group these events in terms ofdesirability. This will result in two or more groups. Packages can thenbe created by taking single events from the different desirabilitylevels. For example, a highly desirable event can be combined with aless desirable event to create a package.

The secondary market can also play an important role in indicating eventdesirability or game quality. In fact, it is sometimes possible to drawa direct correlation between secondary premium and total revenue for anevent. The event desirability or projected total revenue can be used todetermine pricing for the events.

An analytical correlation between ticket prices in the secondary marketand future ticket sales has been developed by the inventors. If ticketsare selling at a large premium in the secondary market, this suggeststhat there will be high demand in the primary market. A mathematicalformula has been created that correlates secondary demand and primarydemand for tickets.

In addition to determining demand, this method also allows for adetermination of fair market pricing. Often tickets are listed for salein the primary market at prices well above those in the secondarymarket, such that the tickets in the primary market will not sell.Conversely, sometimes tickets are on sale in the primary market forprices well below those in the secondary market. The method describedallows for a precise understanding of the fair market price. This can beused to rationalize promotions or create pricing premiums so as tomaximize revenues for a given event.

The method can use either actual secondary ticket sales or it can uselisted tickets on the secondary market, even if they have not sold yet.If listed tickets are used, the data should be corrected for ticketsbeing listed at abnormally high or abnormally low prices. These ticketprices sometimes indicate factors personal to the sellers which shouldnot reasonably be aggregated to determine market supply/demand.

To correct for abnormally high or low prices on the secondary market inone embodiment, all data that is more than 1.5 interquartile ranges fromthe upper or lower quartile is rejected. The interquartile range is thedistance between the lower and upper quartiles. The remaining data isre-examined, and any additional data that falls outside of this limit isalso rejected. This analysis is repeated until all data falls within 1.5interquartile ranges. Other techniques can be used to eliminate unusuallistings.

In a preferred embodiment, the first step is to gather and assembleinformation about tickets and/or sales in the secondary market. This canbe done manually or can be accomplished using automated computersoftware, for example by so-called crawling software that retrievesinformation from one or more public websites accessed via the Internet.In addition, computer software can directly access private databases ofticket information if access has been arranged. Such databases caninteract with the automated computer software via a plurality of meanssuch as SFTP (Secure File Transfer Protocol), direct SQL client-serverinterchange, etc.

FIG. 1 shows one example of a web page of a secondary market fortickets. Different events are listed, and the range of prices for eachevent is summarized for the user.

FIG. 2 shows an example of a web page accessible by clicking on one ofthe events listed in FIG. 1. A list of tickets, accompanied by sectionnumbers, is tabulated. The table has section column 276, row column 278,quantity of tickets for sale column 280 and price column 282. Byclicking on the respective ‘View Details’ link, more information can beobtained about the tickets for sale on the secondary market. Thisinformation can include the exact row and seat number of the seat(s)offered for sale, as well as the ticket ID or whether the seat(s) arelocated on an aisle.

If Internet sites are crawled, such as those with web pages similar toFIGS. 1 and 2, then analysis software in an embodiment first assembles aticket file in native Extensible Markup Language (hereinafter “xml”)code. This native xml file is then parsed by the software to extractrelevant variables such as Ticket ID, Section, Row, Seat (if available),quantity, price, and other special indicators such as whether or not theseat is an aisle seat etc. The software can also use rules coupled withthe known arena seating plan to determine the seat parameters. The xmlticket information, which often contains the seating information infree-form English which is not directly parsable, can be decoded forlater use. For example, a courtside seat may be annotated “CT”, “Court”,“Courtside”, “Floor”, etc. in the xml file. The software can have rulesand intelligence to uniquely decode these annotations. Depending on theamount of seats listed or other factors, either one or more Internetsites may be crawled for data.

FIG. 3 illustrates a seating plan for a basketball game in an arena.Many seats in the arena stay constant from event to event, such as thosein upper sections. Other seats may be arranged differently, such asthose on or near the floor around the basketball court. The software canuse information from past seating arrangements to determine the distancea seat is from the court, from an aisle, and other factors important toconsumers. This distance and other factors are then associated with theseat data from the xml file.

The software also relates the ticket price to the “face value” (or parvalue) of the ticket and the season ticket value of the ticket. Todetermine the season ticket value of a ticket, the price of the seasonticket can be divided by the number of games in the season. The facevalue and season ticket price can usually be obtained from public andprivate sources.

The parsed and decoded ticket data is then stored in a database,including annotations regarding the event ID, game, date, etc. The timeand date of the crawling and analysis can be included to allow forsubsequent analysis of ticket data over time.

The database used can be any one of a number of standard types, such asan ORACLE® database, Microsoft SQL Server® database, or other databasesknown in the art. The database can also be a mix of several databasesand/or database types as well. The use of a database helps store andorganize the data. Other methods of data storage besides databases arecontemplated, such as data files.

Once the ticket database is established it may be queried in a number ofways to extract relevant information. More complex queries are alsopossible, such as the ticket price history over time (e.g., 9 days).

A number of data can be extracted from the database. First, the pricehistory of the ticket over time can be established. If the price historyis changing, then the price history can be trended, and a prediction fora future date established. Second, the true selling price of the ticketcan be established as the price just before the ticket disappears fromthe database. Third, any rapid change of price (e.g., over a few days orhours) can be detected and an automated alarm brought to the attentionof an operator. This may indicate a loss of a key player or otherrelevant external events.

From each data point for a point in time for each seat ticket, adiscount or premium is calculated. A “premium” is a ratio of the pricefor a particular seat ticket in the secondary market over the face valueof the same ticket, preferably expressed as a percentage. A “discount”is simply a negative premium. An equation to determine thediscount/premium is:

premium=(price_(secondary) _(_) _(market)−price_(face) _(_)_(value))price_(face) _(_) _(value)   (Eqn. 1)

where price_(secondary) _(_) _(market) is the price on the secondarymarket and price_(face) _(_) _(value) is the price printed on thephysical ticket stub. A discount/premium can also apply to seasontickets with respect to the season ticket price as opposed to the facevalue.

The secondary market discount or premium can be used to determine thepricing of tickets. If an event is selling for a discount in thesecondary market, then new sales of tickets should also be at a discountto compete with the secondary pricing. The secondary market discount orpremium can be used to determine group pricing and individual pricingstrategies. An event trading at a secondary market discount may be soldto groups at a similar discount. An event trading at a premium in thesecondary market would suggest that a premium should be charged onoriginal ticket sales as well.

To aggregate the many different tickets for each event, the average ofall the premiums in the secondary market(s) can be calculated. Theresulting average premium can be associated with the event for furtheranalysis.

For many sports teams or venues it is optimal to correlate the averagepremium to known results for some number of events. As an example, aplot of average secondary market premiums versus total revenues can bemade for multiple events, and the relationship between secondary marketpremium and the total revenues can be established.

FIG. 4 illustrates the average secondary market premiums as compared tothe total revenues that were obtained for corresponding events. Thediscount/premium for each ticket at an event is averaged into an averagediscount/premium for the event, and the average discount/premium is thenplotted with total revenue for the event on an x, y scatter chart. Itcan be seen from the chart that the games with the largest secondarymarket discount (i.e., a negative premium) had the lowest total revenue.

Line 454 is mathematically fit between the points so that futurerevenues can be predicted. For example, if secondary market salesindicate a discount of −10% from the face value of the tickets, then thetotal revenue can be predicted (from the straight line fit) to be$915,000.

In the figure, a linear equation is fit to the data, although it shouldbe apparent to one skilled in the art that curve fits could take someother functional form (e.g., polynomial). In this particular example,the linear correlation indicates that the total revenue of a futureevent can be calculated from the secondary market premium as totalrevenue =(secondary_market_premium*468.7)/0.0005011, where total revenueis in U.S. dollars and secondary_market_premium is expressed as apercentage. This correlation can then be applied to events in the futurewhere the total revenue is not known but is desired. For example, theresults of the first ten games in a season can be used to predict therevenues for the next fourteen games. The inventors have found inseveral analyses that agreement between the predicted and actualrevenues was within 10% and was better than 1% on average. Thisprediction can be repeated with time as an event approaches as thepremium or discount in the secondary market may change with time. Thecorrelation analysis described above used only the secondary marketpremium, but it could also use additional information like the day ofthe week of the event, the won/loss record of the team, or otherpertinent information. In certain aspects, the exact correlation willnot be the same for each team, performer, or act and will generally needto be determined separately for each case.

FIG. 5 shows such data plotted for an entire basketball season of eventsat an arena (i.e., home games). Some events had already occurred andother were still to be played. The left vertical dashed line indicatesthe minimum revenue that could be collected from each event. Thisminimum is determined from the apportioned revenue of season ticketsthat have already been sold. The right vertical dashed line in thefigure indicates the maximum revenue that could be collected in theprimary market from each event if each and every ticket were sold atface value. The secondary premiums and total revenues for the initialten games that had already been played were analyzed as described aboveand linear fit 586 was determined.

An event is predicted to sell out if the secondary market premium isgreater than the premium indicated by the intersection of the verticalsell out revenue line and linear fit 586 as determined by the premium tototal revenue relationship. Events 584 have a secondary demand thatsuggests that they should sell-out. The three data points correspondingto events 584 do not fall along the sell out vertical dashed line eitherbecause some tickets were subject to group discounts and other pricealterations or because the event had not been played and not all of therevenue had been generated. It should be noted, however, that withoutraising prices above face value, it will not ordinarily be possible togenerate the revenue that the secondary market predicts. The events witha premium below the intersection of the season ticket floor intersectionwith the same fit are indicative of those events that would not attainthe total revenue level without season ticket sales.

FIG. 6 shows the predicted revenue versus the secondary market premiumsas calculated using the equation of linear fit 586 of FIG. 5. Since theprojected revenue for future events can be calculated as soon as thereare tickets for sale in the secondary market, often months before anevent, such information can be used to determine prices and marketingstrategies for future events.

FIG. 6 also shows how events could be separated into different eventquality groups based on their projected total revenue. Thus, games canbe divided into different quality groupings based on their projectedrevenue (or premium or discount). Events with the highest quality (i.e.,highest potential revenues) will be those to the right of the rightvertical dashed line while those with the lowest quality (i.e., lowestpotential revenues) will be to the left of the left vertical dashedline. Events of intermediate quality will fall in between the verticaldashed lines. Using this technique, all events can be grouped into somenumber of quality bands where the number of quality groups is typicallybetween two and ten and more preferably between three and five.

Once the game quality or potential revenue for a series of games isknown, it is possible to group games into packages or to accuratelyprice the full season. High quality events can be bundled with lowerquality events and sold together in order to promote sales or lowerquality (i.e., lower demand) events. Specifically, a package or seasonticket may be determined to be fairly priced when the sum of thepremiums for the games is substantially near zero. For example, one cancombine an event trading at a 25% premium with one trading at a 25%discount so that the resulting package has a value equivalent to theoriginal ticket price. Of course, it may be desirable to offer acustomer a discount for committing to a package of multiple games so thesum of the premium for all games may be chosen to be slightly negative.As the number of the games in the package decreases, the discount (i.e.the sum of premiums) should approach zero.

Accordingly, secondary market data can be used to price tickets, or thedata can be used to bundle tickets. Both methods can be used together aswell to help increase revenue collected in the primary market.

(3) Pricing Sections, Rows, or Seats

Once a user has an idea of the proper price levels to be used fortickets (whether or not the user used the approaches described above),the user is then ready to create a price plan for the event.

An embodiment can be used to create a price plan. The embodiment caninclude downloaded or entered information about the venue in which theevent will be held.

Software can allow the user to assign prices to seats by creating acertain number of ‘price codes’ or price levels that relate to dollarvalues. The software extracts seat information from a venue map, theuser is prompted to assign price codes to sections, rows or seat blockswithin a row, and the dollar value for the price code is associated withthe specific location information. The dollar value of a price code canbe changed centrally, and such changes will update the price for everyseat identified as being part of that price code. The user can also seta price code to ‘kill’ (i.e., indicate that a particular location is notfor sale) while retaining its dollar value. For example, seats withobstructed views can be color coded or X'ed out so that it isimmediately clear to the user that the seats should not be listed forsale. The user may also indicate that certain seats are to be ‘held’(i.e., hold them from an initial sale period). The user can preview avisual representation of the pricing plan to make sure that thelocations by price and the overall financial potential of all tickets(if sold) meet the needs of those involved with the event.

The user can export the price plan's price codes and seat blockassignments to a spreadsheet, such as a Comma-Separated Variable (CSV)file, to be imported into a ticketing system. If the pricing system isdirectly linked to the ticketing system, then the pricing informationcan be directly submitted to the ticketing database. The user can createseveral ‘price plans’ for an event by assigning them different names andcan also share the plans via email or by printing the venue mapassociated with the different price plans.

An embodiment includes software that incorporates a seat level map forthe venue where an event will take place. This software includes adescriptor for each seat in the venue comprised of variables thatuniquely identify that location (e.g., section, row, and seat number).The software also includes a means for ascribing a price to each of theseats. The software integrates this information with seat map softwareand renders a drawing of the proposed seating map by price along withthe economics of the pricing for the venue or by price levels.

Another embodiment includes a system and method for correlating currentseat inventory information with the venue map. The inventory informationmay be uploaded or the system may have a direct connection to theinventory database. This allows the inventory to be displayed visuallyby status (e.g., available, sold, held, killed, inquiry), by price(e.g., the face value of the ticket for a particular seat), and by class(e.g., the type of hold, the type of package associated with the seat).

A database connected to or included in the embodiment can also includethe price for each specific ticket. The information from the databasecan be displayed on a venue map, along with other pertinent informationsuch as total possible revenue, total number of seats at a given price,or total revenue achieved and total remaining potential revenue.

When the display and visualization capabilities are used for planning orinventory control purposes, the seat status information or a data filecan be modified, either manually or through software that allows theuser to select seats on a seating chart. New information (e.g., priceinformation) about those seats can be input, thereby updating the datafile. This allows promoters, venue owners, or other stakeholders arelatively easy to use tool with which to try out different pricing andseating configurations before finalizing pricing for an event.

The display and mapping capability can be accessed through desktopsoftware or through a web-based application. A web-based application canallow users not linked to the ticket data to visualize ticketinformation. This may be particularly valuable to concert promoters thatwish to know about sales status but are not co-located with the ticketdatabase. A web-based system allows users to log in from a remotelocation, enter a password, and view data. In the case of a user thathas access to ticket data, this data is uploaded and the data from thefile can be viewed in a number of displays. For a user who does haveaccess to the ticket inventory database, the embodiment will present thelast data that was uploaded by someone with access. It is also possiblethat the system is connected to the inventory database and informationis updated automatically. There can also be different levels of accessfor different password holders, allowing each user access to apredetermined portion of the data.

FIG. 7 illustrates a web page for defining price levels in accordancewith an embodiment. Web page 100 displays price code table 110. Pricecode table 110 includes price code name column 102, price column 104,kill column 106, and edit link column 108. Price code table 110 includesfive price codes (price codes names “PL1” through “PL3K”). The ‘K’suffix on the end of a PL price level name indicates that the price codeis for killed seats. It may be desirable in some instances to create twoprice categories with different names but the same price. Once the pricecategories are determined, each section (or row or seat) is associatedwith a price category.

Cell 112 indicates the name of the second price code, PL2, andassociated cells 114 and 116 display the corresponding price and whetherthe seat has been killed, respectively. Cell 118 includes a link toanother web page to edit the name, price, or kill status of the pricelevel. A new price code can be added by clicking button 120

FIG. 8A illustrates a web page for correlating price levels of pricecodes with groups of seats in accordance with an embodiment. Price codetable 110 is reproduced on this web page, including cells 112 and 114showing the name and price for price code PL2. Another table on the webpage shows section column 222, seat count column 224, and price codecorrelation column 226. Using dropdown listbox 228, seats in Section 101of the venue, having a seat count of 394 seats, is assigned price levelPL2 (i.e., $85.00). Similarly using dropdown listbox 230, seats inSection 109 are assigned price level PL2K (i.e., $85.00 but not forsale). Changes can be submitted to the server by pressing button 232.

FIG. 8B illustrates a web page for correlating price levels with groupsof seats in accordance with an alternate embodiment. Price code table110 is shown with alternate price codes than those in FIG. 8A. Anothertable shows start section column 223 and end section column 225. Usingthese columns, a user can specify rules for the correlation of pricecodes or levels to particular sections. For example, section 101 throughsection 108 are assigned price code “P1,” and sections 109 through 114are assigned price code “P1” and can be selected as killed. New rulesfor assigning sections can be input by pressing button 233. Similar tothat for sections, rows and individual seats can also have rules forassigning price codes. For example, a rule that individual seats beyond±95° of the front of center stage are automatically killed can be inputby a user so that those seats are not sold.

Rules can also correspond to portions of the venue map. For example, auser might use a mouse to draw an electronic line arcing down betweensections 113 and 114, behind the mixing platform, and then betweensections 129 and 130 (see venue map at lower left of FIG. 8B). The usercould then designate that all sections behind this line be a certainreduced price. In a different embodiment, an performance artist coulduse a light pencil or signature pad to draw his or her own specialsymbol across a swath of seats on the venue map, so as to price theseats within the swath at a discount. This price discount method couldbe used to help market the event as well as build brand awareness forthe artist's special symbol. In another embodiment, the seats within theartist's symbol or mark could be priced at a premium instead of at areduced price. A concert-goer might be enticed to pay slightly more fora ticket to sit in the ‘point’ of the artist's exclamation point or thedot of her signature ‘i.’

FIG. 9 illustrates a venue map with correlated price levels from FIG.8A. Upon submission of the correlated price levels, venue map 900 isdisplayed on a separate web page. Venue map 900 shows the price levelscorrelated with seats. Legend 934 indicates to a user what price levelscorrespond to what graphics.

In certain instances, a price code can be a ‘flex price.’ A flex priceindicates that more than one price level is assigned to a price code.For example, a ‘flex up’ price and a ‘flex down’ price can be input sothat one or the other flex price can be selected during an on-saleevent, depending on demand or sales velocity.

For some ticket vendor software, such as Ticketmaster® ticket salessoftware, tickets cannot be re-priced during a sale, and new ticketscannot be added without stopping the sale to the public. For this andother reasons, it is sometimes helpful to assign multiple prices (e.g.,flex prices) for to the same seat before the sale. In this way, a usercan determine demand based on early sales data and then select onepre-entered price that best matches this demand without halting allsales.

FIG. 10A illustrates a web page for establishing a price code for flexcategories during pricing according to an embodiment. When editing aprice code, a user may check a flex checkbox in order to enable or makevisible textboxes for entry of flex prices. In the exemplary embodiment,two currency input textboxes are shown: one for a ‘flex up’ price, andone for a ‘flex down’ price. More than two input textboxes can be shownfor multiple flex levels. Flex up price level 1014 (i.e., $150) and flexdown price level 1015 (i.e., $100) are input by the user and stored in adatabase. A name indicating that the price code is a flex price code(e.g., “P1P2”, “P1Flex”) can be entered, although any name can beentered. After the price code is newly added or edited, the user submitsthe form using a submit or insert button.

FIG. 10B illustrates a web page for displaying all of the currentlyinput price codes for an event. Column 1002 of the table indicates thename of each price code. Column 1004 indicates the single price or theflex up price for each price code, and column 1005 indicates a flex downprice. A color, hatching, or other indicator can be assigned to eachprice level. FIG. 10B is similar to FIG. 7 but with a column showingflex down prices. Other columns (not shown) can display more than twoflex prices if applicable. More price codes can be added by clicking anew price button.

FIG. 10C illustrates another web page for displaying all of thecurrently input price codes for an event juxtaposed with a venue map.Table 1034 displays flex up and flex down prices. In addition, the tableshows the total value for seats in each price code. Price codes withdifferent flex up and flex down prices show different total values.Price codes with the same prices for flex up and flex down (i.e.,non-flex price codes) show the same total values in the flex up and flexdown total values.

The venue map in the figure shows different seat sections with hatchingindicating the corresponding price level. Other embodiments can showcolors or other indications of the price code down to the seat level.

FIG. 11 is an example flowchart illustrating a process in accordancewith one embodiment. In operation 1102, price levels are provided for anevent at a venue. In operation 1104, rules to attribute one or more ofthe price levels to the seats of the venue are received. In operation1106, each price level is correlated with the seats according to therules. In operation 1108, the price levels are displayed correlated withthe seats on a venue map. In operation 1110, a total projected revenuefor the event is calculated using the price levels. The number of seatscorrelated are also used to calculate the total projected revenue.

Price planning software can be used to visualize and edit pricingconveniently and intuitively. Electronic venue maps, displaying datadown to the seat level, can help promoters, venue representatives, andother stakeholders design price plans efficiently.

(4) Event Tracking

After the planning stage, the seats are set for sale. During the “onsale,” tickets can be bought up extremely quickly by fans or otherdistributors. The first few hours or even minutes of an on-sale can be adynamic confluence of pent-up demand and immediate supply.

A promoter, venue representative, or other stakeholder may wish to watchin real time the sale as it progresses. Some embodiments allow suchaccess on the same screens as indicated above.

Venue maps can allow a user to see all of the current inventory andimmediately distinguish by color, or gray shade, seats that are sold,those that are on hold, and those that are not for sale. In addition,hovering a mouse cursor or other electronic pointer over a seat graphicin the venue map can reveal additional information about the seatstatus, such as the type of hold or selling price of the seat.

Not only can one view information, but the visualization graphics canalso be used during the sale of tickets for inventory control purposes.In this instance, it may be beneficial to display additional informationalong with the venue map or diagram. Such information can include therevenue that a specific venue can generate given a current pricingstructure, the remaining revenue possible from unsold seats, and theamount of revenue that is not accessible due to seats on hold or thatare not for sale.

One embodiment includes a software system and method for storing andcomparing inventory status at different points in time. The systemallows the user to create a map of the inventory status at differenttimes or to display a map that compares the changes that have occurredbetween two or more points in time. This venue map can also be augmentedwith other graphical information about sales as a function of time,which can include the number of seats sold at each time interval intotal or by price, status, or class, the amount of revenue associatedwith tickets sold at each time interval in total or by price, status, orclass, or other time dependent information.

The embodiment can also save inventory information to a history page.Every time the user uploads a new inventory data file or the systemaccesses inventory data from a database, the system saves that data to ahistory file. The user has the ability to go to the history, select thefile, and build a map based upon the data in the file. The data from thehistory files can also be compared to each other. This can yield a newmap that displays inventory changes between the two files or can beshown in graphical form such as total revenue as a function of time.

The embodiment can integrate inventory information from the history fileto form a movie. This movie may be stored and played back as atime-lapse movie (or through high-speed photography techniques) foranalysis. By viewing how the seats fill, one can determine whether theseats fill ‘back to front’ or ‘front to back.’ If the seats fill frontto back, then higher priced seats in the front may not have high enoughprices in relation to lower priced seats in the back. Conversely, if theseats fill back to front, then higher priced seats in the front may havetoo high a price in relation to lower priced seats in the back. One canalso assess ‘side to middle’ and other geographical seating preferencesusing the updating. Effectively, the combined interests of individualticket purchasers are used to inform the viewer of the relative qualityof seats. Taking a snapshot of seat statuses at the beginning and endcan fail to give this type of insight.

Furthermore, watching the fill patterns of the venue's seating caninform a viewer of eccentricities of the venue. For example, if one sideof the venue fills faster than the other side, it could mean that thefirst side of the venue is more convenient to certain types oftransportation, bars and clubs, or other external influences. Also, suchfill patterns may indicate hidden consumer preferences with respect torestroom locations, concession stands, and air conditioning. This canavoid the expense and inaccuracies of customer surveys, focus groups,and other social data gathering techniques which are sometimesineffective.

FIG. 12A illustrates venue map 1200 with seat statuses during a sale oftickets for an event. Legend 1244 indicates seats that are sold, killed,held, or open. Venue map 1200 shows detail down to individual seats thatare sold. This map shows all seats based on their status (open, sold,held, killed) and shows the financial totals for each category (i.e., bysumming the number of seats sold at their respective price, the totaldollar value of the sold seats is calculated).

FIG. 12B illustrates an enlarged portion of region 1240 (section 125 andthe surrounding area of FIG. 12A). The status of each seat can bedisplayed. For example, seat 1242 is displayed as sold. Four rows back,a single seat in the row is still unsold (depicted in this case as acircle with a darker border but could also be based on fill color,shape, or other attribute). Several other single point unsold seats areshown in the section. The unsold seats can indicate problems in bundlingseats for sale. Determining in a section the density of single unsoldseats, which are less likely to sell than two or more seats, can help amarketing team estimate the overall density of single unsold seats forfuture events.

A venue map can also show other statuses of seats, such as whetherpotential buyers have inquired to purchase a seat.

FIG. 12C illustrates the venue map 1200 with seat statuses during a saleof tickets for an event. Table 1246 shows calculated revenue for theevent from the number and price levels of the sold seats. Legend 1248summarizes the number of held, open, and killed seats in the venue.

FIG. 12D illustrates a venue map which may be used to show the status ofseats which are reserved for management, the promoter, record label, andthe artist him or herself. Other types of holds may also exist and belabeled with specific or more general information. Color coding can beemployed so that more categories of stakeholders can be displayed in thelegend and on the venue map. While holds may be created before an eventgoes on sale, the status of holds can change during the course of anevent as seats are released for sale or new seats are held.

FIG. 13 shows an example flowchart illustrating a process in accordancewith another embodiment. In operation 1302, a first status of aninventory of seats of a venue at a first point in time is received. Inoperation 1304, the first status of the inventory of seats is displayedon a venue map. In operation 1306, a second status of the inventory ofseats at a second point in time is received. In operation 1308, thevenue map is updated with the second status of the inventory of seats.In operation 1310, the venue map is refreshed with additional statusesof the inventory of seats. Accordingly, a movie of the statuses is shownon the venue map. In operation 1312, the movie of the statuses on thevenue map is used to predict demand for a future event. For example,back-to-front buying of a past event can indicate that a future eventwill have similar back-to-front demand for seats. Pricing for the backseats can be raised accordingly to even out the demand.

Seat sales can also be visualized in other ways. For example, the numberof tickets sold can be plotted on line, bar, or similar charts withrespect to time. Charts are not limited to tickets sold but couldinvolve the number of inquiries, etc. Also, charts can be created forsubsets of tickets, such as tickets in a certain price level. The usercan also select the time frame over which the inventory data should beplotted. Using such charts, a user can determine if a radioadvertisement resulted in a bump up in ticket sales. Other factors canalso be correlated with ticket sales.

FIG. 14 is a plot of total tickets sold and inquiries with respect totime in accordance with one embodiment of the present invention. In anembodiment, sold tickets 1488 can be shown with inquiries for tickets1490 as shown. Other ticket statuses can also be plotted, such as thosefor held, open, and killed seats.

Watching ticket sales as they progress or plotting sales by time canhelp a promoter, venue representative, or other stakeholder determinemarketing techniques that are working and better estimate demand.However, one can also use the data to act upon the information in realtime to maximize revenue for an event.

(5) Changing Prices or Distribution Channel

As described above, it is preferred that flex price levels be selectedprior to an event. Price codes can be assigned multiple price levels.For example, a price code for a group of seats can include a ‘flex up’price level of $150 and a ‘flex down’ price level of $120. The two pricelevels may correspond with prices in other, non-flex price codes. Forexample, price code “P1/P2” may have a flex up price level equal to thatof price code “P1” (e.g., $150) and a flex down level equal to that ofprice code “P2” (e.g., $120). If one can predict demand for an event,one can use the predicted demand to properly choose to ‘flex up’ or‘flex down.’

During a sale of tickets, it has been found that the first few minutesof sales can be used to predict demand for later on in the same sale.Such real-time predictions may be based on a power model, exponentialgamma model, or other suitable models as will be described.

One embodiment includes systems and methods for determining ticketdemand during an on-sale event then allow a user to adjust salestechniques during the on-sale in response to the demand. This includessoftware that automatically predicts the number of tickets that willsell either in total or by price level. The software can then recommendto the user that certain tickets are moved from one status to another(i.e., flexed from a lower price level to a higher price level or viceversa). In some instances, the software will automatically re-priceinventory without user involvement. Because sales can move rapidly,sometimes automatic re-pricing is preferred.

The system can also be used for predicting the number of ticket salesfor single events, such as a concert on a specific date. Both long termsales (e.g., sales over a period of weeks ending in the event) and shortterm sales (e.g., minute by minute sales starting from the on-sale timeof the event) can be considered. Projecting total sales by price levelallows for seats to be priced at the appropriate level once demand isbetter understood. This flex pricing allows for tickets to be sold atthe highest possible price based on demand. The system can alsodetermine if the event will sell out, either in total or by pricecategory.

Several probabilistic models for the ticket sales modeling arecharacterized by having different hazard functions. The hazard functionis used to determine the duration dependence of ticket sales. Durationdependence is the relationship between time passing and an increased ordecreased likelihood of incremental purchases. Depending upon the hazardfunction, its shape can take several different forms.

Monotonic curves either decrease or increase throughout their duration.A monotonic curve in this application would suggest that the likelihoodof purchasing a ticket is either decreasing or increasing over time. Thedecreasing monotonic curve is appropriate for the short term on-salemodeling. Non-monotonic curves, although more complex, may beappropriate for long term modeling. A U-shaped hazard function can beused where there is an initial rush to secure seats at an event,followed by a lull in sales, and a second rush just before the eventdate.

A simple model for the sale of seats is the exponential model which isgiven by the equation:

S=A(1−e ^(−λt))   (Eqn. 2)

where S is the cumulative ticket sales, A is a constant, λ is aconstant, and t is time from the start of sales. This model has aconstant hazard function of λ and asymptotes to the value of A.Parameters for this simple exponential model can be simply estimated byfitting a least squares straight line to a logarithmic plot of the data.

The simple exponential model's assumption of constant hazard rate is notrealistic for many situations. In sales of sporting event and othertickets, the initial sales rate rapidly drops. To accommodate this, apower model can be used, which is given by the following equation:

S=At^(α)  (Eqn. 3)

where S is the cumulative ticket sales, A is a constant, t is the timefrom the start of sales, and a is a constant. This model has a hazardfunction which is linear. The function asymptotes to infinity.Parameters can be simply estimated by fitting a least squares straightline to a logarithmic plot of the data.

In FIG. 15 the actual number of tickets sold 1560 for an event isplotted with respect to time in comparison with a power model, the powermodel fit to two, three, and four data points. In this example, datapoints are available at 0, 9, 20, 34, 47, and 161 minutes after on-sale.In the exemplary case, two-point prediction 1562 and three-pointprediction 1564 extrapolate to lower ticket sales than there actuallyended up being; however, four-point prediction 1566, using the datapoints at 0, 9, 20, and 34 minutes, matches well to the actual number oftickets sold 1560 curve after 161 minutes.

FIG. 16 shows how the sales velocity or rate of ticket sales (i.e., thenumber of tickets sold per minute or delta per minute) can, for a powermodel, be estimated by straight line fitting on a logarithmic plot. Thelog-log plot of rate versus time offers an elegant way to predict therate of falloff in sales. This can be used to predict when the on-salecan be closed out, i.e., when the rate drops below x sales per minute.

The power model's asymptote at infinity is not altogether realistic, assales, particularly in the long term, will, of course, asymptote tototal seating capacity. Also the linear hazard function is not realisticfor long term sales.

The exponential gamma model allows for these characteristics; λ isdistributed across the population as a gamma distribution. Anexponential gamma model is given by the following equation:

S=A(1−(α/(a−t))^(α))   (Eqn. 4)

where S is the cumulative ticket sales, A is a constant, a is aconstant, a is constant, and t is the time from the start of sales.

The hazard function of the exponential gamma model has a decreasing formproportional to 1/t^(α). This is very suitable for events which exhibitnegative concave duration dependence, such as on-sales. Parameterestimation can be performed by using standard numerical optimizationsoftware. Maximizing the logarithm of the resulting likelihood functionhas been found to be helpful to obtain the maximum likelihood estimatesof the model parameters.

The Weibull-Gamma model is a generalization of the exponential gammamodel. The Weibull-Gamma model allows the hazard rate to vary. It is ofsimilar form to the exponential model (described above), except that tis replaced by t^(c) where c is a constant. The Weibull-Gamma model isgiven by the following equation:

S=A(1−e ^(−λt̂c))   (Eqn. 5)

where S is the cumulative ticket sales, A is a constant, λ is aconstant, t is the time from the start of sales, and c is a constant.

The hazard function of this model is very flexible and can be chosen byvarying parameters. Parameter estimation can be performed by usingstandard numerical optimization software. One can maximize the logarithmof the resulting likelihood function to obtain the maximum likelihoodestimates of the model parameters.

In addition to the single models described above, combinations of modelscan be used, provided enough data is available to accurately estimateparameters. Alternatively, different models can be used at differenttimes in the sale sequence. For example, one model can be used for theon-sale event, one model for long term sales, and another model after anintense marketing campaign.

Calculating how many seats to release at a given ‘flex’ price can bebased on current sales data and other parameters such as number ofinquiries. The relevant future point in time to predict is usually a fewhours into the on-sale. This time is called prediction time t_(p), whichis preferably set to 3 hours into the on-sale. If the model indicatesthat tickets sold P at time t_(p) will be greater than the number ofavailable open seats (O), then the number of flex seats to be releasedshould be equal to P−O (i.e., P minus P), if available. The predictionis adaptive in time. As data points are received, at a preferredgranularity of 5 minutes or faster, a new prediction is made, and theamount of flex seats to be released is updated.

A prediction can also be based on an adaptive exponential functionalmodel, which is given by the equation:

y(t)=A(t)(1−e ^(−λ(t)t))   (Eqn. 6)

where A(t) and λ(t) are parameters that are functions of time, and y(t)is the cumulative ticket sales at time t. Hence the prediction of ticketsold P is given by using the currently estimated parameters, and settingt=t_(p). The hazard function of this model is A(t)*λ(t), and as theseare adaptively modeled in time, almost any shape of hazard function canbe modeled. This is, therefore, a generalization of the previouslydescribed exponential-gamma and Weibull-Gamma modeling methods. Inparticular the characteristic ticket sales rate that occurs during anon-sale in which λ (the ticket sales rate) initially rapidly increasesfor 10-20 minutes then decays, is readily modeled.

The prediction algorithm proceeds as follows. Two data points should beavailable before the above parameters are estimated and the predictionbegins. It is preferable that the data points be at 5 minutes and 10minutes into on-sale so that ticket sales have stabilized. However, itcan be important that the algorithm begin predicting as soon aspossible. It is possible to implement various tests of statisticalconfidence to ensure that the initial two data points used have stablestatistics. Therefore, data points may come in, for example, as fast as1 minute apart, and the algorithm can automatically choose which pointsto use as the initial two-points based on calculated statisticalconfidence.

Best fit may be determined by a simulated annealing algorithm. In asimulated annealing algorithm, parameters A and λ are moved in verysmall increments either up or down from their current values, and if theadaptive exponential function with the new parameters better fits thedata points, then A and/or λ are moved to the new values. This processis repeated.

At each point in time, a new A and λ are calculated from actual datapoints. After all the data points are collected, the different A's andλ's are preserved as A(t) and λ(t). These functions of time can be usedto predict future events.

In one example, data points y₁, and y_(i) where y₁ is the first datapoint at 5 minutes, and y₁ is the current data point (at 10 or greaterminutes). For point 1, the following initialization can be performed:

  A = y₁ * ln( PredictTime ) / ln( t₁ ) Lambda= 1 SumPredict = 0MeanPredict = 0 alpha = .01 AnnealIterations = 1000 TotalReleasedFlex =0 FOR points i >= 2 do the following: yEst₁ = A * (1 - exp( -Lambda * t₁)) yEst_(i) = A * (1 - exp( -Lambda * t_(i) )) RMSerror = sqrt( ( ( y₁ -yEst₁){circumflex over ( )}2 + ( y_(i) - yEst_(i)){circumflex over( )}2) / 2 )

A stochastic simulated annealing algorithm can be implemented toestimate the parameters A and Lambda for the current data point, asfollows:

  FOR 1 to AnnealIterations DO  'vary A first by plus or minus a verysmall number (alpha)  IF RND( 1 ) > 0.5 THEN sign = 1 ELSE sign = -1 NewA = A * (1 + sign * alpha)  'calculate yEst₁, yEst_(i), andNewRMSerror using NewA  yEst₁ = NewA * (1 - exp( -Lambda * _(t1))) yEst_(i) = NewA * (1 - exp( -Lambda * t_(i)))  NewRMSerror = sqrt( ((y₁ - yEst₁){circumflex over ( )}2 + ( y_(i) - yEst_(i)){circumflex over( )}2) / 2)  'use NewRMSerror if a better fit than the previous RMSerror IF NewRMSerror < RMSerror THEN  'accept the NewA as better   A=NewA  RMSerror = NewRMSerror  END IF  'vary Lambda by plus or minus a verysmall number (alpha)  IF RND(1) > 0.5 THEN sign= 1 ELSE sign = -1 NewLambda = Lambda * (1 + sign * alpha)  'calculate yEst₁, yEst_(i),and NewRMSerror usin NewLambda  yEst₁ = A * (1 - exp(-NewLambda * t₁)) yEst_(i) = A * (1 - exp(-NewLambda * t_(i)))  NewRMSerror = sqrt( ( (y₁ - yEst₁){circumflex over ( )}2 + ( y_(i) - yEst_(i)){circumflex over( )}2) / 2 )  'use NewRMSerror if a better fit than the previousRMSerror  IF NewRMSerror < RMSerror THEN  'accept the NewLambda asbetter  Lambda = NewLambda  RMSerror = NewRMSerror  END IF END FOR  'endof the annealing loop

When the annealing loop finishes the parameters A and Lambda areestimated and can be used to form the current prediction. The predictionis calculated by the following:

yPredict_(i) = INT( A * ( 1 - exp( -Lambda * PredictTime) ) ) 'this isthe current point prediction SumPredict = SumPredict + yPredict_(i)MeanPredict = INT(SumPredict / ( i - 1 ) ) 'mean from point 2 to thecurrent point 'Perform a correction on the mean prediction as follows:IF MeanPredict < y_(i) THEN 'prediction should not be less than currentdata point  IF yPredict_(i) > y_(i) THEN   MeanPredict =yPredict_(i)       'use the current prediction instead of the mean  ELSE  MeanPredict = y_(i)           'set equal to current data  END IF ENDIF

MeanPredict is then used to release flex seats:

  CurrentReleaseFlex = ( MeanPredict - StartOpen ) - TotalReleasedFlexIF CurrentReleaseFlex < 0 THEN CurrentReleaseFlex = 0 'cannot un-releaseseats IF ( CurrentReleaseFlex + TotalReleasedFlex ) > StartFlex THEN CurrentReleaseFlex = StartFlex - TotalReleasedFlex 'cannot release >remaining END IF TotalReleasedFlex = TotalReleasedFlex +CurrentReleaseFlex

One can also use the number of inquiries to improve the predictionalgorithm. An inquiry (or enquiry) is a ticket or set of tickets that iscurrently being held for a short amount of time (e.g., 2 minutes) whilea potential customer decides to buy or not to buy. An inquiry can end ifa user declines to buy tickets, if the user buys the ticket, or upontimeout of the hold time. Inquiries are therefore a predictor of salesfor a short time (e.g., 2-10 minutes) into the future. By using aprediction of the number of sales within a short time, Δt, in the futureby using the inquiries, one can improve the prediction model previouslydescribed.

A prediction of the number of tickets to be sold within a short time inthe future can be modeled by the following linear model:

y _(t+Δt) =y _(t)+β_(t) E _(t) Δt   (Eqn. 7)

where y_(t+Δt) is an estimate of the number of tickets sold at a pointt+Δt, y_(t) is the number of tickets currently sold at time t, β_(t) isa parameter that is estimated from the data, E_(t) is the current numberof inquiries, and Δt is a small period of time.

The prediction y_(t+Δt) can be used in the previous algorithm asfollows. Instead of using two points to estimate the parameters A andLambda, one uses three points: y_(t+Δt), y_(i), and y₁. Thus, theprevious annealing algorithm will compute and minimize 3 component errorestimates, i.e.

NewRMSerror=sqrt(((y ₁ −yEst₁)̂2+(y _(i) −yEst)̂2+(y _(i+1)−yEst_(i+1)))/3)

where y_(i+1)=y_(t+Δt).

The parameter β_(i) is estimated as follows.

β_(i)=(y _(i+1) −y _(i))/(t_(i+1) −t _(i))*E_(i))=(dy/dt _(i))/E _(i)

That is, β_(i) is the rate of ticket sales divided by the number ofinquiries. One then can make the estimate that β_(i+1)=β_(i). Then forany point i, the future point y_(i+1)=y_(t+Δt) can be estimated fromcurrent and past data as follows:

y _(i+1) =y _(i)+((y _(i) −y _(i−1))/(t _(i) −t _(i−1))*(E _(i) /E_(i−1)))Δt

where Δt is a small time period between the current point and the futurepoint.

Note that as dy/dt_(i)=(y_(i)−y_(i−1))/(t_(i)−t_(i−1)), one isessentially modeling the future rate of ticket sales as the current ratemodified by the factor E_(i)/ E_(i−1), which is an indicator of thetrend of the level of inquiries. In this way, the number of inquiriescan be used to augment the existing data points with a data pointslightly in the future. The extra data point helps in determining theproper parameters for one of the above models.

FIG. 17 is a flowchart with operations in accordance with an embodiment.In operation 1702, a rate at which a first inventory of seats have soldfor an event at a venue is determined. In operation 1704, a demand iscalculated for a second inventory of seats based on a hazard functionalgorithm which uses the rate at which the first inventory of seatssold, the seats of the first and second inventories being comparable inquality. In operation 1706, one of a plurality of price levels at whichto release the second inventory of seat is determined using the demand.

One or more of the models above can be used to predict the number ofsales for an event while a sale of tickets is in progress. Using thatprediction, one can determine whether to flex up or flex down.

FIG. 18 is a plot of tickets sold on the one-hundred level of a stadiumwith respect to time in accordance with one embodiment of the presentinvention. Also plotted are horizontal lines delineating the predictednumber of seats to be sold 1894, the number of open seats 1896, and thenumber of open seat and flex seats 1898. A user can view the number ofseats sold in time (e.g., pseudo-real time) and determine, based on thepredictions and hard lines, at what time and what price to release theflex seats. The prediction line can be updated with the predictionalgorithms described above.

Inquiries can be used in a different way to estimate the total sales foran event. The inventors have determined that there is a strongcorrelation between the trend of the ratio of tickets sold to inquiriesand the overall number of tickets that will be sold in an on-sale event.

FIG. 19 is a plot of tickets sold versus number of inquiries atdifferent times for a particular event that can be used in accordancewith one embodiment of the present invention. As shown in the figure,the number of inquiries can be matched to the number of tickets that aresold at a given point in time. Data points 1902 are fit to a linearmodel 1904 or exponential model 1906 and interpolated or extrapolated asdesired. Using only a few number of inquiry/tickets sold data points atthe beginning of on-sale (e.g., the rightmost three data points in thefigure), the model can be used to form an independent estimate of thetickets sold at the end of the on-sale. The intercept of the modelfunction with the y-axis (vertical axis) gives the estimate. For linearmodel 1904, this estimate is 619 tickets; the actual number of ticketssold at the end of the on-sale for this event was 657 tickets.

In a preferred embodiment, the model is fitted point by point as datacomes in. In an actual on-sale it is possible that the number ofinquiries increases initially before diminishing. This results in theintercept of the model line not crossing the y-axis in a positivelocation. To correct for this modeling problem, the algorithm can waituntil the number of inquiries decreases, and then declare this point the‘start point.’

A least squares straight line fit can be fit to the inquiry data.Alternatively, a least squares straight line fit can be fit to thenatural log of the data in order to determine an exponential fit.

The linear model, the exponential model, or both can be used to estimatefinal ticket sales. To use both, the prediction from the linear modeland exponential model can be averaged together. An alternative method isto weight the linear model and exponential model differently, where theweightings are determined from the data as it comes in point by point.

The estimate of the final on-sale sales using inquiries can be usedindependently or in conjunction with the previously disclosed methodsfor estimating final sales.

The number of seats that are recommended for release can be correlatedto specific regions of the venue so that tickets are flexed in a uniformmanner. For instance, it may be desirable to flex an entire row at onetime rather than flexing up the price of some seats in a row whileothers are left at a lower price. The flex recommendation can beintegrated with the venue configuration information from the maps.

In addition to flexing entire rows, it may be desirable to flex seats by‘x-number.’ An x-number is a feature that some ticketing software usesto further classify the seats in the venue. Typically, x-numbersidentify which seats are presumed better than other seats. An x-numberdoes not need to include an entire section but can be a sub-section;however, an x-number does not normally include seats in more than onesection. X-numbers are assigned to sections or portions of sections andcan be utilized to prioritize the sale of tickets. It may be desirableto flex seats within one x-number, and this information may also becontained in the venue configuration stored within the system. [0227] Inaddition to the total additional revenue that an event can generate,additional factors may be important in prioritizing sales for flexpricing. One such factor is sales momentum, such as a sell out, whichmay provide potential customers with the indication that the tickets arein limited supply and may also increase the excitement associated withthe event. Also, the remaining inventory may be considered along withthe likelihood that a game will sell out (i.e., the momentum aspect offocus). In practice, group sales usually drop off two to four weeksprior to the event while single event tickets pick up as the eventapproaches. Historical trends regarding single and group event ticketscan be factored in to provide a more accurate indication of additionalforecast ticket sales. In certain aspects, this information can becombined with the total revenue forecasts to create a clear picture oflikely sell outs. If a sell out is likely, then prices can be flexedaccordingly to maximize revenue.

(6) Posting Inventory to the Secondary Market

In addition to or instead of changing or flexing the price of inventoryin the primary market, it may be desirable for a seller in the primarymarket to sell some of the inventory directly on the secondary market.For instance, tickets in the first few rows of floor seats at a concertmay sell for a higher price on the secondary market than the originalface value of the ticket. This may also be true of the first rows, evenin the second or third level of a venue.

To capitalize on the higher prices, key questions the primary ticketseller encounters are: (1) how many and which tickets should be heldfrom the primary market and released to the secondary market, and (2) atwhat price should the tickets be released? [0231] The present inventionprovides algorithms which can recommend answers to the above questionsof “how many tickets should be released?” and “at what price?” Thealgorithms ensure a high probability that these tickets will be amongthe “best” value of all secondary tickets. Because the recommendedtickets will be of greater value than other tickets listed on thesecondary markets for the same event, it is probable that therecommended tickets will actually be sold and realize the potentialextra revenue.

The first step in the algorithm is to produce a list of the tickets onthe secondary market(s) by equivalent or comparable section for theevent. This utilizes the crawling and analysis software previouslydescribed.

Referring back to FIG. 9, comparable sections of seats include thosewith similar viewing quality for the event. For the exemplaryconfiguration in FIG. 9, sections 115 and 128 are comparable to eachother in that they are each an equal distance from the stage. Likewise,sections 117 and 126 are also comparable to each other. However,sections 117/126 would typically be considered as having a higherviewing quality than sections 115/128 because sections 117/126 arecloser to the stage. For sporting events in which the entire field areaof the stadium is the viewing area of interest, section 115, 117, 128,and 126 would all be comparable to each other in that they are an equaldistance from the center point of the field.

Once equivalent sections are defined (if desired) secondary listings forthe event in question are aggregated by equivalent section. The data foreach equivalent section is filtered.

Seat quality can also be objectively determined at the row or seatlevel. For example, one can create a scalar seat quality which takesinto account the distance of the seat from the front of the stage, theheight of the seat above the floor, the distance to an aisle, thedirection of the stage from a straight out view from the seat, and thedeviation from the centerline of the stadium. One can adjust weightingfactors or other linear or non-linear parameters to better align withconsumer preferences from actual data as discussed below.

However, objective determinations of seat quality do not always matchsubjective determinations of seat quality. A myriad of consumerpreferences besides those outlined above often figure into one'sdetermination of a value of a seat. Furthermore, just how much theobjective determinations of seat quality affect one's preferencesremains unknown. For example, a row closer to the field may be desired,but whether one row closer is worth $1, $5, or $50 more is difficult toestablish. The algorithm compensates for many of these unknowns by usingempirical data from the secondary market.

The second step in the algorithm is to model the relationship betweensecondary premium and row number. In general, the premium is highest forrows at the front of a section and decreases with increasing row number.This relationship is modeled using the functional form:

Premium_(row) =A*1/(Row^(alpha))   (Eqn. 8)

with Premium_(row) being the discount/premium calculated above and Rowbeing the row number of the seat. The parameters A and alpha areextracted by using a best least squares fit to the data. Note that ifalpha turns out to be positive, this means that the price is increasingwith increasing row. In this case the model is rejected and noprediction can be made for this equivalent section.

The algorithm then uses the model to determine the price of the ticket,given that it is desired to achieve a certain minimum premium(Premium_(desired)) above face value. For example, this desired premiumcan be chosen as “Aggressive,” “Moderate,” or “Conservative” and set toa certain value, for instance Premium_(desired)=2.5, 2.0, or 1.5respectively, indicating a price of 3.5, 3.0, or 2.5 times face valuerespectively.

Given one or more desired premiums, the algorithm starts at row 1 in asection and computes Premium_(row) for each row using the above model.If Premium_(row)>=Premium_(desired), then it is concluded that ticketscan be released from hold for this row at price (1+Premium_(row))*Face,and have a high probability of actually selling in the secondary market.This is because these tickets will be better than or equal to the bestvalue tickets currently on the secondary market. The result is adetermination of how many rows of tickets could be placed in thesecondary market for each equivalent price section given the desiredminimum premium.

Because ticket data in the “best value” list may be sparse, the modelPremium_(row) may have large errors. For example, if only two tickets inthe secondary market were at rows 15 and 20, the predicted premium forrow 1 may be well off. In order to handle this, the algorithm alsocomputes a “Data Quality” metric, QEQ, for each equivalent section. Ifthis metric is below a threshold, then the data is rejected and nopredictions are made for this comparable section. One preferred metricis given by:

QEQ=1−(Row_(minimum)/Row_(maximum))   (Eqn. 9)

where Row_(minimum) is the row number of the lowest row in thecomparable section list and Row_(maximum) is the row number of thehighest row. This metric lies between 0 and 1 and has a small value ifthe minimum and maximum rows in the data are close together or far fromRow 1.

While the calculations above define the number of rows of tickets, andtherefore the total number of seats, that could be placed on thesecondary market, it is desirable to release or post the tickets to thesecondary market over time so as to not create more supply than demand.

The number of tickets to be released can be computed in various ways.For example, a certain number of tickets can be released at a fixed timeinterval (for instance every hour). It is preferable to release ticketsdepending on the number of tickets currently on the secondary markets soas not to depress prices. For example, the number of tickets that can bereleased can be determined by taking a fixed percentage of currentlistings. Ideally this number would be less than 50% and more likelyless than 20% of the existing secondary listings. This calculation canbe repeated as the listed seats sell, always maintaining a level ofseats lower than some percentage of the total secondary listings. Thismethod could also be further refined by considering the exact locationwithin the venue and maintaining the number of listed tickets below acertain percentage of like tickets.

FIG. 20 shows an example of the algorithm's final recommendations to auser in summary form. Recommendations are shown for floor seating andfixed section seating along with potential secondary revenue for eachseating area. The total potential secondary revenue is also shown.

FIGS. 21A and 21B show example outputs from the algorithm. Tabulated areaggressive (“A”), moderate (“M”), and conservative (“C”) pricing andrevenue potentials. The recommendation sections at the bottom of eachfigure are for different levels of “Aggressive Pricing.” Also shown isthe total potential extra revenue for each scenario. The recommendationsections for all the seating in the venue can be summarized in a webpage such as that shown in FIG. 20.

In one embodiment, a system and method for determining the demand andpricing of inventory in a secondary market is provided. Software usesthis information to determine if inventory should be removed from theavailable primary inventory to be sold on the secondary market. Thesoftware can either make suggestions to the user or make changesautomatically without user involvement. This analysis can provide aspecific value for each seat in the venue. The software can thenrecommend how many seats in each section should be removed frominventory and sold in the secondary market.

The analysis of on-sale demand described above in regard to modifyingprimary ticket pricing during the sale can predict if the event or ifprice sections will sell out. This same information can be used todetermine how many and at which price secondary inventory should bereleased. For instance, if the analysis indicates that a certain pricelevel will quickly sell out, then more inventory from these sectionscould be placed on the secondary market at a higher price. In this way,the calculation of on-sale demand can be coupled with the secondarymarket analysis to automatically determine how aggressively thesecondary market should be utilized..

Software can combine ticket information from the primary and secondarymarkets and display all inventory in a single map. In this way, thetotal revenue from an event, whether derived from primary sales or fromby selling inventory in the secondary market, can be tracked.

(7) Improved Display of Secondary Inventory for Sale

Not only can data from secondary markets be used to project totalrevenue for an event, the data can also be used at the section, row, orseat level to determine the market value of individual seats. The marketvalue can be used to display a ‘best value’ seat or selection of seatsto a user. This allows a user, who may not have the time or expertise todiscern the benefits of a set of seats, to have a more independent basisfor selecting good seats.

FIG. 22A shows ticket premiums gathered and calculated from thesecondary market plotted with respect to row number for a particularsection in an arena. The data is fitted to linear model 2208. This modelcan be used to indicate to a customer where the ‘best value’ seat in thesection is located. Note in the figure that tickets near the front(e.g., rows 2-4) are valued in the secondary market as 250%-300% overthe season ticket price while tickets near the back (e.g., rows 13-14)are valued at less than half the premium (i.e., 50-125%) of the frontseats.

In certain aspects, the deviation of the seat price from the model isdetermined in order to ascertain the best valued ticket available on thesecondary market. For example, the fifth ticket 2206 in FIG. 22B is inrow 6. From the linear model (i.e.,premium_(predicted)=−0.1814*row_number+3.2893), the predicted premium is2.2009 (220%). In contrast, the ticket premium in the secondary marketis 141%. The ticket value, which is the predicted premium minus thepremium in the secondary market, is thus 220%-141%=79% (0.79). The bestvalue ticket will have the highest ticket value and the lowest actualpremium relative to that predicted by the fit for all tickets. Thiscorresponds to the ticket that falls the farthest below the line in FIG.22A. By comparing the ticket value for all tickets in this manner, theticket with the best value when row number is considered is determined.Combining the results of this analysis with the analysis above that didnot consider row number, two tickets for the section that represent thelowest price and the best value are determined. That is, the lowestprice is the ticket on row 14, and the best value ticket is the one onrow 6 (calculated above). Both of these tickets are stored forpresentation to a purchaser.

It is also possible to determine relative value by first finding thelowest priced seat for the lowest listed row within a section orequivalent section. This listing is stored for future display. The nextlowest row with a price lower than the stored listing is also stored.This process is repeated until the last row with a listing for thesection or equivalent section is analyzed. The list of stored seats orlistings can then be presented to a potential purchaser.

For presentation to a purchaser, a venue map can be displayed. The mapcan be produced by software that takes in the locations of seats fromaccurate blueprints or drawings of the arena seating and produces aScalable Vector Graphics (.svg) format file. An .svg file can allow auser to zoom in to the smallest of details in the graphics filedepicting a seat. Zooming in can allow the user to judge such things asthe width, direction, whether there is a cup holder, and other personalconsiderations, if any.

Additional data can be presented to help the purchaser with his or herselection. For example, the average price or premium for tickets in aspecific section or at a particular price point can be provided. Thiscan allow the customer to see the price or premium of the selected seatrelative to the price or premium of other similar seats.

It is possible to further reduce the number of choices that a purchaserhas by comparing the optimal or best value seats to preferences that thepurchaser has provided. One such preference is the number of seats thatthe customer wishes to purchase. In this aspect, any ticket listing witha number of seats below the required number is eliminated, therebyreducing the number of choices. Some other possible preferences arepreferences in cost, value, or closeness to the event and how much thepurchaser is willing to pay.

The user's preferences in terms of price, value, and location can beincorporated into the selection of seats to offer the user by computinga distance metric which gives an indication of the closeness of theparticular seat offering to the user's stated preferences. This metriccan range from 1 (exactly matches the users preference) to 0 (does notmatch at all). A threshold can then be put on the metric to offer onlyseats above the threshold.

In some instances, there are several sections in an arena at the sameoriginal ticket price. Referring back to FIG. 3, sections 101, 102, 119,110, 111, and 112 all have the same original face value ticket price. Insome cases, the value of one section may provide enhanced value relativeto other sections. For instance, sections 101 and 111 may be consideredsuperior sections since they are more centrally located to the court. Inthis case, the relative value can be determined as described above usingonly row number or the premium can be determined using a least squaredanalysis where row and section are treated as variables. In the lattercase, the sections can be assigned a separate value based on theirlocation. In this case, sections 101 and 111 are assigned one value andsections 102, 110, 112, and 119 are assigned a second value. The fit ofpremium to row and section can then be calculated.

If no original pricing information is known, the aggregated ticketprices can be compared on a section by section basis because it wouldnot be possible to determine pricing on an original price basis. Thismay result in more listings than would be provided if the originalticket values were known but relative value of seats within a sectioncan still be determined.

FIG. 23 shows a plot of premiums for seat tickets versus row numbers. Itis apparent from the chart that there is a noticeable correlationbetween the row number and seat price for this example, even though theseats are separated from each other by mere tens of feet.

FIG. 24A and FIG. 24B show one analysis of prices and demand in thesecondary market prior to the on-sale of that event. In this embodiment,this analysis determines the price as a function of row for seats nearthe stage of a concert. In FIG. 24A, data is analyzed for floor seatsfor the two sections at the left and right of the center section. FIG.24B shows the same analysis for the floor seats in the center section.The secondary prices for seats in the same row are higher for the centersection than for the left and right sections.

Although it is common knowledge that center sections are generallypreferred to side sections, it was previously not known how much morecenter sections are worth than side sections. This analysis based onsecondary markets gives concrete estimations of that difference inworth, down to the individual row.

FIG. 25 is a flowchart with operations in accordance with an embodiment.In operation 2502, a list of seat tickets and corresponding prices forsale on a secondary market are received. In operation 2504, the seattickets in the list are grouped by comparable sections of seats. Inoperation 2506, the prices of one of the groups of seat tickets are fitas a function of row to a mathematical model. In operation 2508, a priceis calculated for seats in a row from the mathematical model. Inoperation 2510, an inventory of unsold seat tickets on the row isreceived. The inventory of unsold seat tickets have a predeterminedprice. In operation 2512, it is determined whether the inventory ofunsold seat tickets is priced lower than the calculated price. Inoperation 2514, the inventory of unsold seat tickets is filtered basedon the determination of whether the inventory of unsold seats is pricedlower than the calculated price. In operation 2516, the filteredinventory is then displayed.

FIG. 26 is a block diagram illustrating components of an exemplaryoperating environment in which various embodiments of the presentinvention may be implemented. The system 2600 can include one or moreuser computers, computing devices, or processing devices 2612, 2614,2616, 2618, which can be used to operate a client, such as a dedicatedapplication, web browser, etc. The user computers 2612, 2614, 2616, 2618can be general purpose personal computers (including, merely by way ofexample, personal computers and/or laptop computers running a standardoperating system), cell phones or PDAs (running mobile software andbeing Internet, e-mail, SMS, Blackberry, or other communication protocolenabled), and/or workstation computers running any of a variety ofcommercially-available UNIX or UNIX-like operating systems (includingwithout limitation, the variety of GNU/Linux operating systems). Theseuser computers 2612, 2614, 2616, 2618 may also have any of a variety ofapplications, including one or more development systems, database clientand/or server applications, and Web browser applications. Alternatively,the user computers 2612, 2614, 2616, 2618 may be any other electronicdevice, such as a thin-client computer, Internet-enabled gaming system,and/or personal messaging device, capable of communicating via a network(e.g., the network 2610 described below) and/or displaying andnavigating Web pages or other types of electronic documents. Althoughthe exemplary system 2600 is shown with four user computers, any numberof user computers may be supported.

In most embodiments, the system 2600 includes some type of network 2610.The network may can be any type of network familiar to those skilled inthe art that can support data communications using any of a variety ofcommercially-available protocols, including without limitation TCP/IP,SNA, IPX, AppleTalk, and the like. Merely by way of example, the network2610 can be a local area network (“LAN”), such as an Ethernet network, aToken-Ring network and/or the like; a wide-area network; a virtualnetwork, including without limitation a virtual private network (“VPN”);the Internet; an intranet; an extranet; a public switched telephonenetwork (“PSTN”); an infra-red network; a wireless network (e.g., anetwork operating under any of the IEEE 802.11 suite of protocols, GRPS,GSM, UMTS, EDGE, 2G, 2.5G, 3G, 4G, Wimax, WiFi, CDMA 2000, WCDMA, theBluetooth protocol known in the art, and/or any other wirelessprotocol); and/or any combination of these and/or other networks.

The system may also include one or more server computers 2602, 2604,2606 which can be general purpose computers, specialized servercomputers (including, merely by way of example, PC servers, UNIXservers, mid-range servers, mainframe computers rack-mounted servers,etc.), server farms, server clusters, or any other appropriatearrangement and/or combination. One or more of the servers (e.g., 2606)may be dedicated to running applications, such as a businessapplication, a Web server, application server, etc. Such servers may beused to process requests from user computers 2612, 2614, 2616, 2618. Theapplications can also include any number of applications for controllingaccess to resources of the servers 2602, 2604, 2606.

The Web server can be running an operating system including any of thosediscussed above, as well as any commercially-available server operatingsystems. The Web server can also run any of a variety of serverapplications and/or mid-tier applications, including HTTP servers, FTPservers, CGI servers, database servers, Java servers, businessapplications, and the like. The server(s) also may be one or morecomputers which can be capable of executing programs or scripts inresponse to the user computers 2612, 2614, 2616, 2618. As one example, aserver may execute one or more Web applications. The Web application maybe implemented as one or more scripts or programs written in anyprogramming language, such as Java®, C, C# or C++, and/or any scriptinglanguage, such as Perl, Python, or TCL, as well as combinations of anyprogramming/scripting languages. The server(s) may also include databaseservers, including without limitation those commercially available fromOracle®, Microsoft®, Sybase®, IBM® and the like, which can processrequests from database clients running on a user computer 2612, 2614,2616, 2618.

The system 2600 may also include one or more databases 2620. Thedatabase(s) 2620 may reside in a variety of locations. By way ofexample, a database 2620 may reside on a storage medium local to (and/orresident in) one or more of the computers 2602, 2604, 2606, 2612, 2614,2616, 2618. Alternatively, it may be remote from any or all of thecomputers 2602, 2604, 2606, 2612, 2614, 2616, 2618, and/or incommunication (e.g., via the network 2610) with one or more of these. Ina particular set of embodiments, the database 2620 may reside in astorage-area network (“SAN”) familiar to those skilled in the art.Similarly, any necessary files for performing the functions attributedto the computers 2602, 2604, 2606, 2612, 2614, 2616, 2618 may be storedlocally on the respective computer and/or remotely, as appropriate. Inone set of embodiments, the database 2620 may be a relational database,such as Oracle 10g, that is adapted to store, update, and retrieve datain response to SQL-formatted commands.

FIG. 27 illustrates an exemplary computer system 2700, in which variousembodiments of the present invention may be implemented. The system 2700may be used to implement any of the computer systems described above.The computer system 2700 is shown comprising hardware elements that maybe electrically coupled via a bus 2724. The hardware elements mayinclude one or more central processing units (CPUs) 2702, one or moreinput devices 2704 (e.g., a mouse, a keyboard, etc.), and one or moreoutput devices 2706 (e.g., a display device, a printer, etc.). Thecomputer system 2700 may also include one or more storage devices 2708.By way of example, the storage device(s) 2708 can include devices suchas disk drives, optical storage devices, solid-state storage device suchas a random access memory (“RAM”) and/or a read-only memory (“ROM”),which can be programmable, flash-updateable and/or the like.

The computer system 2700 may additionally include a computer-readablestorage media reader 2712, a communications system 2714 (e.g., a modem,a network card (wireless or wired), an infra-red communication device,etc.), and working memory 2718, which may include RAM and ROM devices asdescribed above. In some embodiments, the computer system 2700 may alsoinclude a processing acceleration unit 2716, which can include a digitalsignal processor DSP, a special-purpose processor, and/or the like.

The computer-readable storage media reader 2712 can further be connectedto a computer-readable storage medium 2710, together (and, optionally,in combination with storage device(s) 2708) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information. Thecommunications system 2714 may permit data to be exchanged with thenetwork and/or any other computer described above with respect to thesystem 2700.

The computer system 2700 may also comprise software elements, shown asbeing currently located within a working memory 2718, including anoperating system 2720 and/or other code 2722, such as an applicationprogram (which may be a client application, Web browser, mid-tierapplication, RDBMS, etc.). It should be appreciated that alternateembodiments of a computer system 2700 may have numerous variations fromthat described above. For example, customized hardware might also beused and/or particular elements might be implemented in hardware,software (including portable software, such as applets), or both.Further, connection to other computing devices such as networkinput/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disk (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, data signals, datatransmissions, or any other medium which can be used to store ortransmit the desired information and which can be accessed by thecomputer. Based on the disclosure and teachings provided herein, aperson of ordinary skill in the art will appreciate other ways and/ormethods to implement the various embodiments.

In the foregoing specification, the invention is described withreference to specific embodiments thereof, but those skilled in the artwill recognize that the invention is not limited thereto. Variousfeatures and aspects of the above-described invention may be usedindividually or jointly. Further, the invention can be utilized in anynumber of environments and applications beyond those described hereinwithout departing from the broader spirit and scope of thespecification. The specification and drawings are, accordingly, to beregarded as illustrative rather than restrictive.

FIG. 28 indicates how a user would interface with the softwareapplication. When a user downloads the application or enters the webpage, they are asked if they would like to register and can do soindependently or through their Facebook account. If they registerthrough Facebook, the software automatically downloads their Likes tohelp establish preferences. The software can also access informationfrom music libraries such as iTunes on their mobile device. A user canchoose not to establish preferences but by establishing preferences thesoftware can recommend events especially tailored to the users desireswithout further interaction. Alternatively, a user can answer a seriesof questions that will help establish their preferences.

If a user wants to attend an event with a friend or group of friends,the software application can also integrate preferences and recommendevents that are most likely to appeal to the group of people that areplanning on going. These preferences can be established from Facebook,from a similar social media network, or via simple questions. Thesoftware application provides a short list of optimal events based onlocation and preference. A user can select an event and then sees avenue map with the best tickets available for purchase. A user canselect a seat and is directed to a purchase page within the applicationor is directed to the appropriate external site to complete thepurchase.

FIG. 29 provides an indication of the back end processes of thesoftware. When a user downloads the software application or links to theweb site, they are asked if they would like to register and can do soindependently or through their Facebook or similar social media account.If they register through Facebook or other social media network, thesoftware accesses their Likes to help establish preferences.

The software can also access information from music libraries on theirmobile device. A user can choose not to establish preferences but byestablishing preferences the software can recommend events especiallytailored to the users desires. If a user wants to attend an event with afriend or group of friends, the software can also integrate preferencesand recommend events that are most likely to appeal to the group ofpeople that are planning on going. The software can also accessinformation about previously defined groups from one or more socialnetworks.

The information about preferences, groups, and friends preferences canbe stored locally or retrieved in each instance that the application isaccessed.

The application provides a short list of optimal events based onlocation and preference. Once a user identifies an event of interest,the software can also determine how this event compares to a thepreferences of others in the user's social network. The software canrank each person in the social network as to their likely interest inthe selected event.

Once an event is selected, a user can sees a venue map with the besttickets available for purchase. If a user selects an event where thereis more than one such event (e.g. a Broadway production which has manysimilar shows on different days) the software can aggregate data frommultiple events, either automatically or those chosen by a user, anddisplay best available tickets from a number of events in a single view.A user can select a seat from the map display and is directed to apurchase page or to the appropriate web site to complete the purchase.

In an example, the present invention provides the following methodoutlined by the steps below:

output an indication on the display for a social networking account froman event application;

select a link associated with the social networking account to connectthe event application to the social networking account;

output a preference indication from the events application for inputtinga plurality of preferences for a plurality of events;

display a graphical indication associated with each event from theplurality of events, each graphical indication including a sliding scalefrom a least desirable spatial region to a most desirable spatialregion;

select a degree of desirability ranging from the least desirable spatialregion to the most desirable spatial region on the indication of theevent;

repeat the displaying and the selecting for other events;

associate the degree of desirability for each of the events to thesocial networking account; and

perform other steps, as desired.

As shown, the above sequence of steps provides a method according to anembodiment of the present invention. Depending upon the embodiment, anyof the steps can be combined, separated, or reordered. Steps and/orother features can be added or varied depending upon the specificembodiment. Of course, there can be other variations, modifications, andalternatives.

In an alternative example, the present invention provides the followingmethod outlined by the steps below:

initialize a social networking application provided on a display of themobile device, the social networking application;

receive login information from a user at a ticketing application fromthe social networking application;

retrieve a list of interests at the ticketing application from thesocial networking application;

retrieve a list of events at the ticketing application;

process the list of events to filter the list of events to select afiltered listing of the events to be outputted to the user of the mobiledevice;

output the filtered listing of events on the display of the mobiledevice;

select an event from the list of events;

retrieve a plurality of tickets for the selected event;

process, under control of a processor, the plurality of tickets for theselected events to select a listing of tickets;

present on the display the listing of tickets to the user on a venueimage associated with the event;

allow the user to select at least one of the tickets in the listing;

direct the user to a process for purchasing the selected tickets;

identify a second user having a second list of interests;

process the list of interests of the user with the second list ofinterests of the second user;

determine whether the second list of interests and the list of interestsare within a predetermined criteria;

invite the second user by transferring invitation information initiatedfrom the ticketing application to a mobile device of the second user;whereupon the second user and the user are associated as friends in thesocial networking application;

transfer invitation information by the first user to one or more usersdefined as friends in the social networking application; and

perform other steps, as desired.

As shown, the above sequence of steps provides a method according to anembodiment of the present invention. Depending upon the embodiment, anyof the steps can be combined, separated, or reordered. Steps and/orother features can be added or varied depending upon the specificembodiment. Of course, there can be other variations, modifications, andalternatives.

In an alternative example, the present invention provides a method ofdetermining selected users of a social networking site to invite to anevent from a plurality of events and of purchasing tickets to the eventusing a mobile device, which is outlined below:

-   -   retrieve a list of events from a ticketing application;    -   select an event from the list of events;    -   retrieve a first list of first preferences a first user;    -   associate the first user with at least two users;    -   determine a level of interest for each of the two users to        attend the selected event;    -   output a list of users interested in attending the selected        event;    -   retrieve a plurality of tickets for the selected event;    -   process the plurality of tickets, using a best value process, to        filter the plurality of tickets to provide a filtered listing of        tickets;    -   receive information from a selected ticket from the filtered        listing of tickets; and initiate a payment process for the        selected ticket.

As shown, the above sequence of steps provides a method according to anembodiment of the present invention. Depending upon the embodiment, anyof the steps can be combined, separated, or reordered. Steps and/orother features can be added or varied depending upon the specificembodiment. Of course, there can be other variations, modifications, andalternatives.

Ticketing systems are currently designed to display available inventoryfor a single event at a time. Often, however, the consumer is flexibleenough that they could go to any one of several events. For instance, aconsumer may be interested in going to a baseball game during a certainhomestand when the local team is playing a visiting team on three orfour consecutive dates. The consumer may be flexible enough to go to anyof those games and wants to find the best ticket. Currently, theconsumer would have to look at the available inventory for each gameindividually and remember what is available from one game to the next inorder to find the best ticket and then would have to go back to the gamewith the best ticket and hope that that ticket is still available forpurchase. Similarly, a tourist traveling to New York may be interestedin seeing a certain Broadway play while they are in New York but hasflexibility as to the show they can see. Again, the consumer would haveto look at the available inventory for each event separately and thenchoose the event with the best ticket.

The current invention allows a consumer to compare ticket inventoryacross two or more similar events. In the simplest case, the differentevents would be the same show or the same game with the same opponentsin the same venue. The first step is for the consumer to select eventsthat they may be interested in attending. The inventory for all selectedevents is grouped and a best ticket algorithm is applied to the list oftickets from multiple events. The events could also be groupedautomatically based on a date range or some other criteria, eitherchosen automatically in software or selected by the user. The besttickets are then displayed, either in a list or in a visualrepresentation of the venue. The tickets are differentiated so that theuser can correlate each of the tickets with a given date of theperformance.

If a user selects events that are significantly different and where theprices for the different events are very different, the best ticketalgorithm may need to be modified to reflect these divergent values. Forinstance, if a user wants to see a basketball game over the weekend andthe opponents to the home team are very different, then the prices mayalso be very different. To compare how good a deal is to these differentgames requires that the prices be normalized relative to the averageprice for each game. For instance, if a ticket in Section 101 Row 5Seats 10 and 11 are $100 to one game and $75 to the second game then itmight appear that the second ticket is a better value. However, if theaverage price for all tickets to first game is $120 and to the secondgame is $80 then the ticket to the first game is actually a better valuethan the second game (i.e. the ticket price for the first game is lessthan the average price where the ticket price for the second game isgreater than the average price). More complex analysis is possible inorder to ensure an accurate comparison.

Once a set of tickets are determined to have optimal value, these ticketchoices can be displayed to the purchaser through the web site. Anexample of such a list is shown in FIG. 31. Each optimal seat can alsobe displayed in a view of the venue. The arena display consists of afile in Scalable Vector Graphics (.svg) format (Ref:http://en.wikipedia.org/wiki/Scalable Vector Graphics). The file can beproduced by proprietary software that takes in the locations of seatsfrom accurate blueprints or drawings of the arena seating and producesthe desired file.

A portion of the file is shown below:

  <?xml version=“1.0” encoding=“UTF-8” standalone=“no”?> <!-- Createdwith Inkscape (http://www.inkscape.org/) --> <svg  xmlns:dc=“http://purl.org/dc/elements/1.1/”  xmlns:cc=“http://web.resource.org/cc/”  xmlns:rdf=“http://www.w3.org/1999/02/22-rdf-syntax-ns#”  xmlns:svg=“http://www.w3.org/2000/svg”  xmlns=“http://www.w3.org/2000/svg”  xmlns:sodipodi=“http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd”  xmlns:inkscape=“http://www.inkscape.org/namespaces/inkscape”  id=“svg2”   sodipodi:version=“0.32”   inkscape:version=“0.45.1”  width=“9600”   height=“7199”   version=“1.0”  sodipodi:docbase=“C:\Documents and Settings\User\Desktop\True ViewBeginnings”   sodipodi:docname=“Clippers Arena Auto Seat LabeledBackup.svg”  inkscape:output_extension=“org.inkscape.output.svg.inkscape”> <metadata     id=“metadata7”>    <rdf:RDF>      <cc:Work       rdf:about=“”>       <dc:format>image/svg+xml</dc:format>      <dc:type        rdf:resource=“http://purl.org/dc/dcmitype/StillImage” />     </cc:Work>     </rdf:RDF>  </metadata>  <defs     id=“defs5” /> <sodipodi:namedview     inkscape:window-height=“984”    inkscape:window-width=“1680”     inkscape:pageshadow=“2”    inkscape:pageopacity=“0.0”     guidetolerance=“10.0”    gridtolerance=“10.0”     objecttolerance=“10.0”    borderopacity=“1.0”     bordercolor=“#666666”    pagecolor=“#ffffff”     id=“base”     inkscape:zoom=“0.10737602”    inkscape:cx=“4800”     inkscape:cy=“3599.5”    inkscape:window-x=“-4”     inkscape:window-y=“-4”    inkscape:current-layer=“svg2”     showguides=“true”    inkscape:guide-bbox=“true” />  <rect    style=“fill:gray;fill-opacity:.5”     id=“101CT-A-1”    width=“35.999722”     height=“29.999722”     x=“4349.0815”    y=“-5122.7612”     rx=“7.3107791”     ry=“7.3107786”    transform=“matrix(7.1937329e-3,0.9999741,-0.9999741,7.1937329e-3,0,0)”    inkscape:label=“#101CT-A-1” />  <rect    style=“fill:gray;fill-opacity:.5”     id=“101CT-A-2”    width=“35.999722”     height=“29.999722”     x=“4348.9063”    y=“-5091.2041”     rx=“7.3107791”     ry=“7.3107786”    transform=“matrix(7.1937323e-3,0.9999741,-0.9999741,7.1937323e-3,0,0)”    inkscape:label=“#101CT-A-2” />

The file can be displayed in whole or in part on any browser that is.svg capable. Furthermore, the display can be zoomed in and out toprovide local or “global” views of the area of interest of the arena.This enables the user to rapidly compare different ticket offerings fromthe software.

The portion of the file below shows the data for one seat:

  <rect  style=“fill:gray;fill-opacity:.5”  id=“101CT-A-1” width=“35.999722”  height=“29.999722”  x=“4349.0815”  y=“-5122.7612” rx=“7.3107791”  ry=“7.3107786” transform=“matrix(7.1937329e-3,0.9999741,-0.9999741,7.1937329e-3,0,0)” inkscape:label=“#101CT-A-1” />

The id of the seat is given in Section-Row-Seat format e.g. 101CT-A-2above, which is the first seat in the first Courtside row “A” in section101. Also specified is the location and size of the seat to be drawn.

The color of the seat is given by the codestyle=“fill:gray;fill-opacity:.5”.

FIG. 35 shows a CMYK color spectrum and an RGB color spectrum accordingto an embodiment of the present invention. The color of the seat isgiven by the code style=“fill:gray;fill-opacity:.5”. The software canmodify the color code so that a wide variety of information can bedisplayed to the user. For example, seats can be colored to indicateprice, value, or closeness to the users preferences. Different colorspectrum codings can be used to indicate “hot” or “desirable” to “cool”or “undesirable” as shown in FIG. 35.

The purchaser can then scroll or mouse over a ticket from the list. Thiswill highlight the location within the venue. This can also generate aview from the seat as is shown in FIG. 31. Alternatively, the purchasercan scroll or mouse over a seat within the venue and the seat will behighlighted in the list and a view from the seat can be presented. Inthis way, it is easy or a purchaser to compare seats available forpurchase. It is also possible to show only the venue view and to thenpresented the specific ticket information as the purchaser scrolls overthe seat in the venue view.

Additional data could be presented to help the purchaser with theirselection. For instance, the average price or premium for tickets in aspecific section or at a particular price point could be provided. Thiswould allow the customer to see the price or premium of the selectedseat relative to the price or premium of other similar seats.

It is possible to further reduce the number of choices that a purchaserhas by comparing the optimal seats to preferences that the purchaser hasprovided. One such preference is the number of seats that the customerwishes to purchase. Any ticket listing with a number of seats below therequired number could be eliminated reducing the number of choices. Someother possible preferences are listed in the questionnaire in FIG. 32.By having the user complete a questionnaire of this type, thepurchaser's preferences can be compared to the list of optimal ticketsto further reduce the ticket choices.

The user's preferences in terms of Price, Value, and Location can beincorporated into the selection of seats to offer the user by computinga distance metric M which gives an indication of the closeness of theparticular seat offering to the user's stated preferences. This metriccan range from 1 (exactly matches the users preference) to 0 (does notmatch at all). A threshold can then be put on the metric to offered onlyseats above the threshold.

The overall metric M can be made up of individual metrics for Price,Value, and Location, and others.

For Price, the “distance metric” between the seat price P_(s) and thedesired price P_(d) can be given by the normalized absolute difference|P_(s)−P_(d)|/P_(max). Similarly, for Value the metric could be:|V_(s)−V_(d)|/V_(max). For Location the actual distance in seats couldbe computed using a “city block” distance=seats difference+rowdifference |S_(s)−S_(d)|/S_(max).

These individual metrics can then be weighted by the stated preferencesfor Price, Value, and Location (w_(p), w_(v), w_(s)) to produce theoverall Metric M:

M=w _(p) *|P _(s) −P _(d) |/P _(max) +w _(v) *|V _(s) −V _(d) |/V _(max)+w _(s) *|S _(s) −S _(d) |/S _(max)

Other more complex metrics are possible using pattern recognitiontechniques. For example, the available seat offerings can be consideredto have the “features” or “attributes” of Price, Value, Location, andother parameters. These features form a multi-dimensional feature spacethat may be non-linear, with the available seats forming points in thefeature space. The users preferences can then input into the space andthe nearest features (available seats) can be output by the softwareusing a number of Pattern Recognition techniques such as k-nearestneighbors, Support Vector Machines, Fisher Linear Discriminant,Principle Component Analysis, etc. These methods can be used to reducethe number of possible seat choices presented to a purchaser.

FIGS. 33 and 34 illustrate simplified flows of a process according to anembodiment of the present invention. Referring now to FIGS. 33 and 34,purchasing of event tickets can be a challenging process for customers,especially on a mobile device. That is because mobile purchasingplatforms do not allow users to select their seats from a venue map.There are significant challenges in presenting a user the ability toselect seats on a mobile device including the need to zoom and pan thevenue image to allow seats to be seen clearly and selected easily.Technologically, this is complicated by the fact that different browsersand mobile devices do not support certain technologies. The presentinvention solves these challenges and provides and improved purchasingprocess on a mobile device.

While there may be several steps prior to the actual event selection, atsome point a user selects an event of interest or the user is buyingtickets to one event (i.e. they did not have a choice of events). Atthis point, the user is ideally presented a map of the venue where theseats that are available for purchase are indicated, and ideally, thedifferent prices for the different locations of available seats are alsopresented. The present invention relies on a venue map that has beencreated as a scalable vector graphic. The map can be delivered to themobile device, either through a content delivery network (CDN), or froma single server. The advantage of a CDN is that a server may be moreclosely located to a user and thereby improve the speed of download ofthe map.

If the map is being delivered from a dedicated server, the map caneither be delivered to the device with the current inventory informationor the inventory information can be updated dynamically from the device.In the former case, when the user selects the event, the selectiontriggers a call to the server that houses the venue map(s). The actionwill also trigger a call to the same or a different server that containsthe inventory information for the event. The .svg file will be modifiedso as to reflect the inventory information that was retrieved from theinventory database. For instance, the Section, Row, and Seat for allavailable seats will be retrieved along with the price for eachavailable seat. The .svg attribute for each available seat will bealtered by comparing the section, row and seat information from theinventory database to an object identifier for each seat object withinthe .svg thereby correlating the information in the inventory databaseto the seat objects in the .svg. Once the proper seat object isidentified, the attributes for the seat object will be modified (e.g. toreflect that it is available and to reflect the selling price). Theattribute can be altered using cascading style sheets in line in the.svg document or can alter a reference that is contained in an externalstyle sheet. Once the .svg has been altered to reflect the inventoryinformation that is to be displayed, the .svg is inserted in line in thehtml document of the venue display page. The venue map is then displayedto the user with the appropriate inventory highlighted for easyvisualization by the user.

A second alternative is to deliver the .svg from a dedicated server butto insert the .svg drawing in line in the html document and thenseparately modify the .svg via javascript so as to reflect the inventoryinformation retrieved from the inventory database. The inventory datamay be sent in a JSON format. The advantage of this approach is that the.svg is delivered to the display device only once and then changes tothe .svg can be done dynamically. This can speed up the display ofinformation and reduce the amount of data transferred between thevisualization device (e.g . mobile device) and the server.

A final approach to delivering the venue map is by using a contentdelivery network (CDN). The overall process is the same as the secondalternative described above but, in the case of the CDN, the .svg mapmay be delivered from one of several different servers. By housing themaps in a network, it is more likely that a server will be close to theuser thereby reducing download times. The rest of the process isvirtually unchanged.

Once a venue map is displayed, the user interacts with the map to selectthe seats that they are interested in purchasing. On smaller devices(e.g. phones) it may be necessary or desirable to zoom in to seeindividual seats more clearly and to select individual seats. There aretwo methods that can be used to accomplish this zooming. In the firstcase, the entire page can be zoomed using the pinching zoom feature on asmart phone. In this case, it is important that the original .svg imagebe >500 pixels and more advantageously around 1000 pixels so that mapcan be zoomed sufficiently.

An alternative to zooming the entire page is to provide the .svg withinan html window where the window area can be zoomed but other regions ofthe page cannot be zoomed. This allows the user to expand the map viewbut to keep the price legend or basket info and continue button the samesize and visible even when the map is being zoomed.

When a user finds a seat they are interested in purchasing, they clickor tap on the seat in the venue map. This action activates a javascriptevent handler code which examines the seat object attributes such as ID,class, etc to determine if the seat is available for purchase. If theseat is not available for purchase then the tap or click event isignored but if the seat is available for purchase then the click or tapevent results in the code checking to determine if the seat is alreadyin the basket. If the seat not already in the basket then the seatattributes will be modified to indicate that it has been selectedwhereas if it had been previously selected then the clicking or tappingwill result in the seat being deselected. There are two ways that thisselection/deselection can occur. In a high band width, low latencysituation, the action of selecting/deselecting a seat can result in animmediate call to the inventory server to reserve the seat and to add tothe users basket. If the bandwidth is low or the latency is high thenthe seat selection/deselection can result in the seat being added to astore on the local device only. In both cases the appearance of the seaton the device can be modified to indicate that a selection was made andadditional text or other information about the selection can be added tothe html page. In the low band width/high latency scenario, the actualcall to the remote server is only made once the user clicks on thecontinue button to purchase the selected seat.

In a very high latency scenario (e.g. concert on-sale) it may beadvantageous to provide the user a specific set of seats based on theprice they are willing to pay, the section they want to sit in, or on abest available basis rather than having them choose from a venue map. Inthat case, the user would be provided with a venue map view of the seatsthat were selected. In certain instances, it may also be advantageous toshow them a set of the same number of seats but at a higher price levelso the user can compare the seats selected to a higher priced selection.It may also be advantageous to provide a view from one or both of thepossible choices as shown in FIG. 31.

The rest of the purchase process follows a traditional ecommerce checkout process. The user may input personal information such as their name,address, phone number, or zip code along with payment information suchas a credit card number, expiration date, and security code, as well asa method for ticket delivery such as mail, will call, oremail/electronic. Once entered, the system will complete the processingof the order, deliver the tickets to the user (if email or electronicdelivery was selected) and send out notifications and confirmation ofthe purchase as needed.

In an example, the present invention provides a mobile computing device.The device has a display comprising a surface region and a back plane, aprocessor configured with the display, a memory device coupled to theprocessor, and a scalable vector graphic (SVG) computer code, undercontrol of the processor device, configured to output an interactivevenue map image comprising at least one of either a graphic object, avector graphic, a raster graphic, or text. The device has a plurality ofgraphical objects configuring the interactive venue map image to depicta seat or row, and each of the plurality of objects being either a pathor a shape. In an example, the venue map is associated with a liveevent, which may include at least an actor or actress, an athlete, amusician, or other performer.

In an example, the path or shape comprises a fill or a stroke where thefill represents the center of the shape or path and the strokerepresents the outline of the shape or path. In an example, the textcomprises a section label. In an example, the scalable vector graphiccomputer code is XML. In an example, the seat or row objects contain ahyperlink to a URL. In an example, the image is configured from the SVGcomputer code inline in an HTML5 web page. In an example, the device hasan event handler configured with JAVASCRIPT to an image object toprovide interactivity to a seat map using a tap, click or other touchscream based event. In an example, the touch screen based event is atleast one of a start, a move and end to providing interactivity for agesture, which is at least one of a swiping or a zooming. In an example,the SVG computer code is configured to color code a seat map torepresent availability or a price level. In an example, the devicefurther comprising a matrix configured on an SVG computer codecomprising a graphic element. In an example, the device uses a matrix toon an SVG element or uses a web kit CSS transform to alter an image onthe display within a view box for to cause simulating a pan across aseat map to view different seats or to simulate a zoom within the seatmap for more detail. In an example, the device comprises a handlerconfigured to trigger a transform based on the handler caused from atap, a click and another touch screen based events including at leastone of a start, a move and an end. The the SVG computer code comprisesthe venue map in a .svg format to output an optical seat within thevenue map. In an example, the venue map is configured to be zoomed inand out to provide a local or a global view of an area of interestwithin the venue map. In an example, the SVG computer code comprises atleast one of style=“”, id=“”, width=“”, height=“”, x=“”, and y=“”. In anexample, the SVG computer code comprises a section-row-seat-format and asize and a location of a particular set to be drawn. In an example, theparticular seat is configured with a color code to indicate a price,value, or closeness to a user preference. In an example, the deviceincludes a scroll or a mouse configured to highlight a list and a viewfrom a particular seat. In an example, the device has a venue viewconfigured to be displayed upon a scroll over a particular seat. In anexample, the device has a display of ticket information upon the scrollover the particular seat. In an example, the ticket informationcomprises a price or a premium of the particular seat to other similarseats.

In an alternative example, the present invention provides a methodoutputting an image of a venue map on a mobile device. The methodincludes providing an visual image of a venue map on a display having afixed frame of a mobile device. The mobile device comprises a processor,a transmitter, and a memory device. The visible image is retrieved andoutputted the display. The venue map comprises an image of a pluralityof individual seats of the venue map and a legend region within avicinity of the plurality of individual seats or a basket region. Themethod includes providing an input to the mobile device, under controlof the processor, to move the plurality of individual seats from a firstseat to a second seat using a panning action relative to the fixed frameof the display or increasing a size of at least one or more of theindividual seats using a zooming action relative to the fixed frame ofthe display. The method also includes simultaneously with the panningaction or zooming action of the plurality of individual seatsmaintaining the legend region or the basket region in a fixed spatialposition relative to the fixed frame of the display.

In an example, the venue map is interactive, under control of theprocessor, and wherein the providing the input comprises selecting oneof the the plurality of individual seats by a user to output anindication of the venue map; and further comprising deselecting theselected plurality of individual seats by tapping on a portion of thedisplay within a proximity of the selected seat. In an example, themethod further comprises triggering a purchase indication of theselected seat in the basket region. The selected seat is deselected bycausing an interaction with the basket region of the display. In anexample, the method further comprises outputting at least one price inthe legend region for the selected seat of the venue map. In an example,the plurality of individual seats in the venue map are color coded;wherein the legend region outputs additional information associated withthe selected seat based the color of the selected seat.

FIG. 36 illustrates a method of using an outputted ticket to access agate to an event venue in accordance with one embodiment of the presentinvention. As shown, a user 3101 can use an outputted ticket 3110 on amobile device 3120 to access a turnstile gate 3130 to an event venue3140, or other type of gate, door, or secured entry or exit mechanism.Although the outputted ticket 3110 is shown as a digital QR code shownon the display of the mobile device 3120, the outputted ticket 3110 canalso be a physical printed ticket and can also include bar codes,Datamatrix codes, Microsoft “tags,” and the like. Here, the turnstilegate 3130 includes a scanner 3131 and a turnstile barrier 3132, therotating bars, that allows or restricts access in either direction. Inthis case, when the user scans the outputted ticket 3110 on theturnstile scanner 3131, the outputted ticket 3110 actuates a sensor toinitiate the release of a locking mechanism on the turnstile barrier3132, allowing the user to move through the turnstile gate 3130 toaccess the event venue 3140. In an alternative example, the gate canopen automatically or perform other functions to allow or deny entry ofthe user with the outputted ticket. In an example, access is denied ifthe outputted ticket is invalid in some manner, that is, not authorized,or used, or other imperfection, among others. Of course, there can beother variations, modifications, and alternatives.

FIG. 37 illustrates a method of using an outputted ticket to access adispenser within an event venue in accordance with one embodiment of thepresent invention. As shown, a user 3101 can use the same outputtedticket 3110, as in FIG. 36, to access the selection interface 3151 of avending machine 3150. The gate structure can be a vending machineinterface 3151 that includes a scanner 3152, a plurality of dispensingsprings 3153, and a collection slot 3154. The outputted ticket 3110 canunlock the vending machine interface 3151 to provide the user access tothe selection of drinks or snacks that are stored on the dispensingsprings 3153. Once the user selects a particular drink or snack or otheritem, the vending machine 3150 dispenses the item for collection by theuser. In an example, when the user scans the outputted ticket 3110 onthe vending scanner 3151, the outputted ticket 3110 actuates a sensor toinitiate the rotating of at least one of the dispensing springs 3153,releasing one of the stored items and dropping the item into thecollection slot 3154. Of course, there can be other variations,modifications, and alternatives.

In an example, methods of the present invention can further includeoutputting at least one the selected tickets as an outputted ticket andinitiating an entry process to a gate structure at an event venueassociated with the selected tickets. The entry process can includeusing the outputted ticket to access the gate structure. The gatestructure can include an access control gate, a turnstile, a vendingmachine interface, a gaming machine interface, a room door, amerchandise distribution interface, a parking gate, a locker, or apersonal storage unit, or the like. Also, the gate structure can includeany other type of gate, door, or secured entry or exit mechanism. In aspecific example, the outputted ticket, digital or printed, can be usedto unlock or lock a gate structure to deny access or allow entry throughthe gate structure. In an example, the locking/unlocking mechanism isprovided by a device comprising a mechanical latch, a magnetic lock, anelectrical lock, or other latch or lock mechanism. The locking/unlockingmechanism can also include a dispensing spring, a movable gate arm, acomputer chip, or the like. In the unlock case, the outputted ticket canprovide access to receive items such as food, beverages, video games,memorabilia, and the like. The outputted ticket can provide access toenter into a restricted space such as a room, a parking lot, anelevator, and the like. In the lock case, the outputted ticket canprovide a means to restrict access to items or spaces, as describedpreviously. Those of ordinary skill in the art will recognize othervariations, modifications, and alternatives.

In an example, the gate structure comprises an access control gate, aturnstile, a vending machine interface, a gaming machine interface, aroom door, a merchandise distribution interface, a parking gate, alocker, or a personal storage unit. In an example, the entry processuses the ticket to unlock or lock the gate structure, whereupon theunlocking occurs by actuating a sensor to initiate release of a device,comprising a mechanical latch, a movable gate arm, a magnetic lock, orelectrical lock, to unlock the gate structure; whereupon the lock occursby maintaining the device in a locked state to prevent entry through thegate.

In an example, the present invention provides a computer-implementedmethod for determining a number of open seats to be allocated for saleat a given price for an event during an initial sales period using aticketing system programmed by a computer readable memory to perform themethod. In an example, the method includes one or more or all of thefollowing steps:

-   -   providing, by a processor of the ticketing system, a ticketing        web interface to a computing device via the Internet using a        communications module of the ticketing system;    -   placing, by the processor, an initial set of a plurality of open        seats for sale at a first price via the ticketing web interface        using the communications module, the initial set of the        plurality of open seats having an initial number of open seats;    -   storing, by the processor, information associated with the        initial set of the plurality of open seats in a first portion of        the computer readable memory;    -   reserving, by the processor, at least one seat within a vicinity        to each open seat from the initial set of the plurality of open        seats such that each seat within the vicinity to each open seat        from the initial set of the plurality of open seats is placed in        a hold status, the hold status being not available for sale via        the ticketing web interface;    -   identifying, by the processor, a first number of open seats from        the initial set of the plurality of open seats that have been        sold or are being considered for purchase via the ticketing web        interface using the information associated with the initial set        of the plurality of open seats in the first portion of the        computer readable memory;    -   calculating, by the processor, a second number of the plurality        of open seats to sell at a determined point in time based on a        time dependence of the first number of open seats sold or being        considered for purchase;    -   determining, by the processor, if the initial number of open        seats placed for sale at the first price is smaller than the        second number of open seats that is calculated to sell, and    -   when it is determined that the second number of open seats        projected to sell is greater than the initial number of open        seats placed for sale, releasing, by the processor, a first        released set of the seats placed in the holding status within        the vicinity of each open seat of the initial set of the        plurality of open seats for sale at the first price; and    -   outputting, by the processor, the number of seats that have been        released to the ticketing web interface using the communications        module,    -   wherein the outputting of the number of seats that have been        released causes the ticketing system to update the ticketing web        interface using the communications module to display on the        computing device with the released seats for sale and to enable        a user of the computing device to purchase seats for the event        from an updated set of seats, including the initial set of the        plurality of open sets and the first released set of seats, at        the first price following the initial sales period;    -   outputting to a user a ticket associated with one of the number        of seats that have been released; and

using the ticket to open a gate structure associated with a venue forthe event to allow the user to enter into the venue.

In an example, the present method also includes outputting a ticketassociated with one of the individual seats; and using the ticket toaccess a gate structure associated with a venue of the venue map tounlock the gate structure or deny access to the gate structure. In anexample, the system also has a particular seat provided within the venuemap. In an example, the particular seat is associated with a ticket topurchase the particular seat. In an example, the system has a gatestructure associated with a venue for the event to allow the user toenter into the venue using the ticket. Of course, there can be othervariations, modifications, and alternatives.

Various additional objects, features and advantages of the presentinvention can be more fully appreciated with reference to the detaileddescription and accompanying drawings that follow. Additionally, Exhibit1, which is incorporated by reference herein, provides a graphical userdisplay and navigation according to other embodiments of the presentinvention.

What is claimed is:
 1. A system comprising: a mobile computing devicecomprising: a display comprising a surface region and a back plane; aprocessor configured with the display; a memory device coupled to theprocessor; a scalable vector graphic (SVG) computer code, under controlof the processor device, configured to output an interactive venue mapimage comprising at least one of either a graphic object, a vectorgraphic, a raster graphic, or text; a plurality of graphical objectsconfiguring the interactive venue map image to depict a seat or row, andeach of the plurality of objects being either a path or a shape;whereupon the venue map being associated with a live event, the liveevent including at least an actor or actress, an athlete, a musician, orother performer; a particular seat provided within the venue map, theparticular seat being associated with a ticket to purchase theparticular seat; and a gate structure associated with a venue for theevent to allow the user to enter into the venue using the ticket.
 2. Thesystem of claim 1 wherein the path or shape comprises a fill or a strokewhere the fill represents the center of the shape or path and the strokerepresents the outline of the shape or path; wherein the gate structurecomprises an access control gate, a turnstile, a vending machineinterface, a gaming machine interface, a room door, a merchandisedistribution interface, a parking gate, a locker, or a personal storageunit; wherein the gate structure is opened using an entry process, theentry process uses the ticket to unlock or lock the gate structure,whereupon the unlocking occurs by actuating a sensor to initiate releaseof a device, comprising a mechanical latch, a movable gate arm, amagnetic lock, or electrical lock, to unlock the gate structure;whereupon the lock occurs by maintaining the device in a locked state toprevent entry through the gate.
 3. The system of claim 1 wherein thetext comprises a section label.
 4. The system of claim 1 wherein thescalable vector graphic computer code is XML; wherein the gate structurecomprises an access control gate, a turnstile, a vending machineinterface, a gaming machine interface, a room door, a merchandisedistribution interface, a parking gate, a locker, or a personal storageunit; wherein the gate structure is opened using an entry process, theentry process uses the ticket to unlock or lock the gate structure,whereupon the unlocking occurs by actuating a sensor to initiate releaseof a device, comprising a mechanical latch, a movable gate arm, amagnetic lock, or electrical lock, to unlock the gate structure;whereupon the lock occurs by maintaining the device in a locked state toprevent entry through the gate.
 5. The system of claim 1 wherein theseat or row objects contain a hyperlink to a URL.
 6. The system of claim1 an image configured from the SVG computer code inline in an HTML5 webpage.
 7. The system of claim 1 further comprising an event handlerconfigured with JAVASCRIPT to an image object to provide interactivityto a seat map using a tap, click or other touch scream based event, thetouch screen based event being at least one of a start, a move and endto providing interactivity for a gesture, the gesture being at least oneof a swiping or a zooming.
 8. The system of claim 1 wherein the SVGcomputer code is configured to color code a seat map to representavailability or a price level.
 9. The system of claim 1 furthercomprising a matrix configured on an SVG computer code comprising agraphic element.
 10. The system of claim 1 further comprising using amatrix to on an SVG element or using a web kit CSS transform to alter animage on the display within a view box for to cause simulating a panacross a seat map to view different seats or to simulate a zoom withinthe seat map for more detail.
 11. The system of claim 1 furthercomprising a handler configured to trigger a transform based on thehandler caused from a tap, a click and another touch screen based eventsincluding at least one of a start, a move and an end.
 12. The system ofclaim 1 wherein the SVG computer code comprises the venue map in a .svgformat to output an optical seat within the venue map; wherein the gatestructure comprises an access control gate, a turnstile, a vendingmachine interface, a gaming machine interface, a room door, amerchandise distribution interface, a parking gate, a locker, or apersonal storage unit; wherein the gate structure is opened using anentry process, the entry process uses the ticket to unlock or lock thegate structure, whereupon the unlocking occurs by actuating a sensor toinitiate release of a device, comprising a mechanical latch, a movablegate arm, a magnetic lock, or electrical lock, to unlock the gatestructure; whereupon the lock occurs by maintaining the device in alocked state to prevent entry through the gate.
 13. The system of claim1 wherein the venue map is configured to be zoomed in and out to providea local or a global view of an area of interest within the venue map.14. The system of claim 1 wherein the SVG computer code comprises atleast one of style=“”, id=“”, width=“”, height=“”, x=“”, and y=“”. 15.The system of claim 1 wherein the SVG computer code comprises asection-row-seat-format and a size and a location of the particular seatto be drawn.
 16. The system of claim 15 wherein the particular seat isconfigured with a color code to indicate a price, value, or closeness toa user preference.
 17. The system of claim 1 further comprising a scrollor a mouse configured to highlight a list and a view from the particularseat.
 18. The system of claim 1 further comprising a venue viewconfigured to be displayed upon a scroll over the particular seat. 19.The system of claim 18 further comprising a display of a ticketinformation upon the scroll over the particular seat.
 20. The system ofclaim 19 wherein the ticket information comprises a price or a premiumof the particular seat to other similar seats.
 21. A method outputtingan image of a venue map on a mobile device, the method comprising:providing an visual image of a venue map on a display having a fixedframe of a mobile device, the mobile device comprising a processor, atransmitter, and a memory device, the visible image being retrieved andoutputted the display, the venue map comprising an image of a pluralityof individual seats of the venue map and a legend region within avicinity of the plurality of individual seats or a basket region;providing an input to the mobile device, under control of the processor,to move the plurality of individual seats from a first seat to a secondseat using a panning action relative to the fixed frame of the displayor increasing a size of at least one or more of the individual seatsusing a zooming action relative to the fixed frame of the display;simultaneously with the panning action or zooming action of theplurality of individual seats maintaining the legend region or thebasket region in a fixed spatial position relative to the fixed frame ofthe display; outputting a ticket associated with one of the individualseats; and using the ticket to access a gate structure associated with avenue of the venue map to unlock the gate structure or deny access tothe gate structure.
 22. The method of claim 21 wherein the venue map isinteractive, under control of the processor, and wherein the providingthe input comprises selecting one of the the plurality of individualseats by a user to output an indication of the venue map; and furthercomprising deselecting the selected plurality of individual seats bytapping on a portion of the display within a proximity of the selectedseat.
 23. The method of claim 22 further comprising triggering apurchase indication of the selected seat in the basket region.
 24. Themethod of claim 23 wherein the selected seat is deselected by causing aninteraction with the basket region of the display.
 25. The method ofclaim 21 further comprising outputting at least one price in the legendregion for the selected seat of the venue map.
 26. The method of claim21 wherein the plurality of individual seats in the venue map are colorcoded; wherein the legend region outputs additional informationassociated with the selected seat based the color of the selected seat;wherein the gate structure comprises an access control gate, aturnstile, a vending machine interface, a gaming machine interface, aroom door, a merchandise distribution interface, a parking gate, alocker, or a personal storage unit; wherein the gate structure is openedusing an entry process, the entry process uses the ticket to unlock orlock the gate structure, whereupon the unlocking occurs by actuating asensor to initiate release of a device, comprising a mechanical latch, amovable gate arm, a magnetic lock, or electrical lock, to unlock thegate structure; whereupon the lock occurs by maintaining the device in alocked state to prevent entry through the gate.